`) const parent = CONTAINER.parentElement // We use a controller to remove event listeners upon invalidation const controller = new AbortController() // We have to add this to keep supporting @bind with the old API using PLOT PLOT.addEventListener('input', (e) => { CONTAINER.value = PLOT.value if (e.bubbles) { return } CONTAINER.dispatchEvent(new CustomEvent('input')) }, { signal: controller.signal }) // This create the style subdiv on first run firstRun && CONTAINER.appendChild(html` `) let original_height = plot_obj.layout.height let original_width = plot_obj.layout.width // For the height we have to also put a fixed value in case the plot is put on a non-fixed-size container (like the default wrapper) // We define a variable to check whether we still have to remove the fixed height let remove_container_size = firstRun let container_height = original_height ?? PLOT.container_height ?? 400 CONTAINER.style.height = container_height + 'px' // We create a Promise version of setTimeout function delay(ms) { return new Promise((resolve) => setTimeout(resolve, ms)); } // We import interact for dragging/resizing const { default: interact } = await import('https://esm.sh/interactjs@1.10.19') function getImageOptions() { const o = plot_obj.config.toImageButtonOptions ?? {}; return { format: o.format ?? "png", width: o.width ?? original_width, height: o.height ?? original_height, scale: o.scale ?? 1, filename: o.filename ?? "newplot", }; } const CLIPBOARD_HEADER = CONTAINER.querySelector(".plutoplotly-clipboard-header") ?? CONTAINER.insertAdjacentElement( "afterbegin", html`` ); function checkConfigSync(container) { const valid_classes = [ "missing-config", "matching-config", "different-config", ]; function setClass(cl) { for (const name of valid_classes) { container.classList.toggle(name, name == cl); } } // We use the custom getters we'll set up in the container const { ui_value, config_value, config_span, key } = container; if (config_value === undefined) { setClass("missing-config"); config_span.innerHTML = `The key ${key} is not present in the config.`; } else if (ui_value == config_value) { setClass("matching-config"); config_span.innerHTML = `The key ${key} has the same value in the config and in the header.`; } else { setClass("different-config"); config_span.innerHTML = `The key ${key} has a different value (${config_value}) in the config.`; } // Add info about setting and unsetting config_span.insertAdjacentHTML( "beforeend", `
Click on the label once to set the current UI value in the config.` ); config_span.insertAdjacentHTML( "beforeend", `
Click twice to remove this key from the config.` ); } const valid_formats = ["png", "svg", "webp", "jpeg", "full-json"]; function initializeUIValueSpan(span, key, value) { const container = span.closest(".clipboard-span"); span.contentEditable = key === "format" ? "false" : "true"; let parse = (x) => x; let update = (x) => (span.textContent = x); if (key === "width" || key === "height") { parse = (x) => Math.round(parseFloat(x)); } else if (key === "scale") { parse = parseFloat; } else if (key === "format") { // We remove contentEditable span.contentEditable = "false"; // Here we first add the subspans for each option const opts_div = span.appendChild(html`

`); for (const fmt of valid_formats) { const opt = opts_div.appendChild( html`${fmt}` ); opt.onclick = (e) => { span.value = opt.textContent; }; } parse = (x) => { return valid_formats.includes(x) ? x : localValue; }; update = (x) => { for (const opt of opts_div.children) { opt.classList.toggle("selected", opt.textContent === x); } }; } else { // We only have filename here } let localValue; Object.defineProperty(span, "value", { get: () => { return localValue; }, set: (val) => { if (val !== "") { localValue = parse(val); } update(localValue); checkConfigSync(container); }, }); // We also assign a listener so that the editable is blurred when enter is pressed span.onkeydown = (e) => { if (e.keyCode === 13) { e.preventDefault(); span.blur(); } }; span.value = value; } function initializeConfigValueSpan(span, key) { // Here we mostly want to define the setter and getter const container = span.closest(".clipboard-span"); Object.defineProperty(span, "value", { get: () => { return plot_obj.config.toImageButtonOptions[key]; }, set: (val) => { // if undefined is passed, we remove the entry from the options if (val === undefined) { delete plot_obj.config.toImageButtonOptions[key]; } else { plot_obj.config.toImageButtonOptions[key] = val; } checkConfigSync(container); }, }); } const config_spans = {}; for (const [key, value] of Object.entries(getImageOptions())) { const container = CLIPBOARD_HEADER.querySelector(`.clipboard-span.${key}`); const label = container.querySelector(".label"); // We give the label a function that on single click will set the current value and with double click will unset it label.onclick = DualClick( () => { container.config_value = container.ui_value; }, (e) => { console.log("e", e); e.preventDefault(); container.config_value = undefined; } ); const ui_value_span = container.querySelector(".clipboard-value"); const config_value_span = container.querySelector(".config-value") ?? label.insertAdjacentElement( "afterbegin", html`` ); // Assing the two spans as properties of the containing span container.ui_span = ui_value_span; container.config_span = config_value_span; container.key = key; config_spans[key] = container; if (firstRun) { plot_obj.config.toImageButtonOptions = plot_obj.config.toImageButtonOptions ?? {}; // We do the initialization of the value span initializeUIValueSpan(ui_value_span, key, value); // Then we initialize the config value initializeConfigValueSpan(config_value_span, key); // We put some convenience getters/setters // ui_value forward Object.defineProperty(container, "ui_value", { get: () => ui_value_span.value, set: (val) => { ui_value_span.value = val; }, }); // config_value forward Object.defineProperty(container, "config_value", { get: () => config_value_span.value, set: (val) => { config_value_span.value = val; }, }); } } // These objects will contain the default value // This code updates the image options in the PLOT config with the provided ones function setImageOptions(o) { for (const [key, container] of Object.entries(config_spans)) { container.config_value = o[key]; } } function unsetImageOptions() { setImageOptions({}); } const set_button = CLIPBOARD_HEADER.querySelector(".clipboard-span.set"); const unset_button = CLIPBOARD_HEADER.querySelector(".clipboard-span.unset"); if (firstRun) { set_button.onclick = (e) => { for (const container of Object.values(config_spans)) { container.config_value = container.ui_value; } }; unset_button.onclick = unsetImageOptions; } // We add a function to check if the clipboard is popped out CONTAINER.isPoppedOut = () => { return CONTAINER.classList.contains("popped-out"); }; CLIPBOARD_HEADER.onmousedown = function (event) { if (event.target.matches("span.clipboard-value")) { console.log("We don't move!"); return; } const start = { left: parseFloat(CONTAINER.style.left), top: parseFloat(CONTAINER.style.top), X: event.pageX, Y: event.pageY, }; function moveAt(event, start) { const top = event.pageY - start.Y + start.top + "px"; const left = event.pageX - start.X + start.left + "px"; CLIPBOARD_HEADER.style.left = left; CONTAINER.style.left = left; CONTAINER.style.top = top; } // move our absolutely positioned ball under the pointer moveAt(event, start); function onMouseMove(event) { moveAt(event, start); } // We use this to remove the mousemove when clicking outside of the container const controller = new AbortController(); // move the container on mousemove document.addEventListener("mousemove", onMouseMove, { signal: controller.signal, }); document.addEventListener( "mousedown", (e) => { if (e.target.closest(".plutoplotly-container") !== CONTAINER) { cleanUp(); controller.abort(); return; } }, { signal: controller.signal } ); function cleanUp() { console.log("cleaning up the plot move listener"); controller.abort(); CLIPBOARD_HEADER.onmouseup = null; } // (3) drop the ball, remove unneeded handlers CLIPBOARD_HEADER.onmouseup = cleanUp; }; function sendToClipboard(blob) { if (!navigator.clipboard) { alert( "The Clipboard API does not seem to be available, make sure the Pluto notebook is being used from either localhost or an https source." ); } navigator.clipboard .write([ new ClipboardItem({ // The key is determined dynamically based on the blob's type. [blob.type]: blob, }), ]) .then( function () { console.log("Async: Copying to clipboard was successful!"); }, function (err) { console.error("Async: Could not copy text: ", err); } ); } function copyImageToClipboard() { // We extract the image options from the provided parameters (if they exist) const config = {}; for (const [key, container] of Object.entries(config_spans)) { let val = container.config_value ?? (CONTAINER.isPoppedOut() ? container.ui_value : undefined); // If we have undefined we don't create the key. We also ignore format because the clipboard only supports png. if (val === undefined || key === "format") { continue; } config[key] = val; } Plotly.toImage(PLOT, config).then(function (dataUrl) { fetch(dataUrl) .then((res) => res.blob()) .then((blob) => { const paste_receiver = document.querySelector('paste-receiver.plutoplotly') if (paste_receiver) { paste_receiver.attachImage(dataUrl, CONTAINER) } sendToClipboard(blob) }); }); } function saveImageToFile() { const config = {}; for (const [key, container] of Object.entries(config_spans)) { let val = container.config_value ?? (CONTAINER.isPoppedOut() ? container.ui_value : undefined); // If we have undefined we don't create the key. if (val === undefined) { continue; } config[key] = val; } Plotly.downloadImage(PLOT, config); } let container_rect = { width: 0, height: 0, top: 0, left: 0 }; function unpop_container(cl) { CONTAINER.classList.toggle("popped-out", false); CONTAINER.classList.toggle(cl, false); // We fix the height back to the value it had before popout, also setting the flag to signal that upon first resize we remove the fixed inline-style CONTAINER.style.height = container_rect.height + "px"; remove_container_size = true; // We set the other fixed inline-styles to null CONTAINER.style.width = ""; CONTAINER.style.top = ""; CONTAINER.style.left = ""; // We also remove the CLIPBOARD_HEADER CLIPBOARD_HEADER.style.width = ""; CLIPBOARD_HEADER.style.left = ""; // Finally we remove the hidden class to the header CLIPBOARD_HEADER.classList.toggle("hidden", true); return; } function popout_container(opts) { const cl = opts?.cl; const target_container_size = opts?.target_container_size ?? {}; const target_plot_size = opts?.target_plot_size ?? {}; if (CONTAINER.isPoppedOut()) { return unpop_container(cl); } CONTAINER.classList.toggle(cl, cl === undefined ? false : true); // We extract the current size of the container, save them and fix them const { width, height, top, left } = CONTAINER.getBoundingClientRect(); container_rect = { width, height, top, left }; // We save the current plot size before we pop as it will fill the screen const current_plot_size = { width: PLOT._fullLayout.width, height: PLOT._fullLayout.height, }; // We have to save the pad data before popping so we can resize precisely const pad = {}; pad.unpopped = getSizeData().container_pad; CONTAINER.classList.toggle("popped-out", true); pad.popped = getSizeData().container_pad; // We do top and left based on the current rect for (const key of ["top", "left"]) { const start_val = target_container_size[key] ?? container_rect[key]; let offset = 0; for (const kind of ["padding", "border"]) { offset += pad.popped[kind][key] - pad.unpopped[kind][key]; } CONTAINER.style[key] = start_val - offset + "px"; if (key === "left") { CLIPBOARD_HEADER.style[key] = CONTAINER.style[key]; } } // We compute the width and height depending on eventual config data const csz = computeContainerSize({ width: target_plot_size.width ?? config_spans.width.config_value ?? current_plot_size.width, height: target_plot_size.height ?? config_spans.height.config_value ?? current_plot_size.height, }); for (const key of ["width", "height"]) { const val = target_container_size[key] ?? csz[key]; CONTAINER.style[key] = val + "px"; if (key === "width") { CLIPBOARD_HEADER.style[key] = CONTAINER.style[key]; } } CLIPBOARD_HEADER.classList.toggle("hidden", false); const controller = new AbortController(); document.addEventListener( "mousedown", (e) => { if (e.target.closest(".plutoplotly-container") !== CONTAINER) { unpop_container(); controller.abort(); return; } }, { signal: controller.signal } ); } CONTAINER.popOut = popout_container; function DualClick(single_func, dbl_func) { let nclicks = 0; return function (...args) { nclicks += 1; if (nclicks > 1) { dbl_func(...args); nclicks = 0; } else { delay(300).then(() => { if (nclicks == 1) { single_func(...args); } nclicks = 0; }); } }; } // We remove the default download image button plot_obj.config.modeBarButtonsToRemove = _.union( plot_obj.config.modeBarButtonsToRemove, ["toImage"] ); // We add the custom button to the modebar plot_obj.config.modeBarButtonsToAdd = _.union( plot_obj.config.modeBarButtonsToAdd, [ { name: "Copy PNG to Clipboard", icon: { height: 520, width: 520, path: "M280 64h40c35.3 0 64 28.7 64 64V448c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128C0 92.7 28.7 64 64 64h40 9.6C121 27.5 153.3 0 192 0s71 27.5 78.4 64H280zM64 112c-8.8 0-16 7.2-16 16V448c0 8.8 7.2 16 16 16H320c8.8 0 16-7.2 16-16V128c0-8.8-7.2-16-16-16H304v24c0 13.3-10.7 24-24 24H192 104c-13.3 0-24-10.7-24-24V112H64zm128-8a24 24 0 1 0 0-48 24 24 0 1 0 0 48z", }, direction: "up", click: DualClick(copyImageToClipboard, () => { popout_container(); }), }, { name: "Download Image", icon: Plotly.Icons.camera, direction: "up", click: DualClick(saveImageToFile, () => { popout_container({ cl: "filesave" }); }), }, ] ); function getOffsetData(el) { let cs = window.getComputedStyle(el, null); const odata = { padding: { left: parseFloat(cs.paddingLeft), right: parseFloat(cs.paddingRight), top: parseFloat(cs.paddingTop), bottom: parseFloat(cs.paddingBottom), width: parseFloat(cs.paddingLeft) + parseFloat(cs.paddingRight), height: parseFloat(cs.paddingTop) + parseFloat(cs.paddingBottom), }, border: { left: parseFloat(cs.borderLeftWidth), right: parseFloat(cs.borderRightWidth), top: parseFloat(cs.borderTopWidth), bottom: parseFloat(cs.borderBottomWidth), width: parseFloat(cs.borderLeftWidth) + parseFloat(cs.borderRightWidth), height: parseFloat(cs.borderTopWidth) + parseFloat(cs.borderBottomWidth), } }; if (el === PLOT) { // For the PLOT we also want to take into account the offset odata.offset = { top: PLOT.offsetParent == CONTAINER ? PLOT.offsetTop : 0, left: PLOT.offsetParent == CONTAINER ? PLOT.offsetLeft : 0, } } return odata; } function getSizeData() { const data = { plot_pad: getOffsetData(PLOT), plot_rect: PLOT.getBoundingClientRect(), container_pad: getOffsetData(CONTAINER), container_rect: CONTAINER.getBoundingClientRect(), }; return data; } function computeContainerSize({ width, height }, sizeData = getSizeData()) { const computed_size = computePlotSize(sizeData); const offsets = computed_size.offsets; const plot_data = { width: width ?? computed_size.width, height: height ?? computed_size.height, }; return { width: (width ?? computed_size.width) + offsets.width, height: (height ?? computed_size.height) + offsets.height, noChange: width == computed_size.width && height == computed_size.height, } } // This function will change the container size so that the resulting plot will be matching the provided specs function changeContainerSize({ width, height }, sizeData = getSizeData()) { if (!CONTAINER.isPoppedOut()) { console.log("Tried to change container size when not popped, ignoring"); return; } const csz = computeContainerSize({ width, height }, sizeData); if (csz.noChange) { console.log("Size is the same as current, ignoring"); return } // We are now going to set he width and height of the container for (const key of ["width", "height"]) { CONTAINER.style[key] = csz[key] + "px"; } } // We now create the function that will update the plot based on the values specified function updateFromHeader() { const header_data = { height: config_spans.height.ui_value, width: config_spans.width.ui_value, }; changeContainerSize(header_data); } // We assign this function to the onblur event of width and height if (firstRun) { for (const container of Object.values(config_spans)) { container.ui_span.onblur = (e) => { container.ui_value = container.ui_span.textContent; updateFromHeader(); }; } } // This function computes the plot size to use for relayout as a function of the container size function computePlotSize(data = getSizeData()) { // Remove Padding const { container_pad, plot_pad, container_rect } = data; const offsets = { width: plot_pad.padding.width + plot_pad.border.width + plot_pad.offset.left + container_pad.padding.width + container_pad.border.width, height: plot_pad.padding.height + plot_pad.border.height + plot_pad.offset.top + container_pad.padding.height + container_pad.border.height, }; const sz = { width: Math.round(container_rect.width - offsets.width), height: Math.round(container_rect.height - offsets.height), offsets, }; return sz; } // Create the resizeObserver to make the plot even more responsive! :magic: const resizeObserver = new ResizeObserver((entries) => { const sizeData = getSizeData(); const {container_rect, container_pad} = sizeData; let plot_size = computePlotSize(sizeData); // We save the height in the PLOT object PLOT.container_height = container_rect.height; // We deal with some stuff if the container is poppped CLIPBOARD_HEADER.style.width = container_rect.width + "px"; CLIPBOARD_HEADER.style.left = container_rect.left + "px"; config_spans.height.ui_value = plot_size.height; config_spans.width.ui_value = plot_size.width; /* The addition of the invalid argument `plutoresize` seems to fix the problem with calling `relayout` simply with `{autosize: true}` as update breaking mouse relayout events tracking. See https://github.com/plotly/plotly.js/issues/6156 for details */ let config = { // If this is popped out, we ignore the original width/height width: (CONTAINER.isPoppedOut() ? undefined : original_width) ?? plot_size.width, height: (CONTAINER.isPoppedOut() ? undefined : original_height) ?? plot_size.height, plutoresize: true, }; Plotly.relayout(PLOT, config).then(() => { if (remove_container_size && !CONTAINER.isPoppedOut()) { // This is needed to avoid the first resize upon plot creation to already be without a fixed height CONTAINER.style.height = ""; CONTAINER.style.width = ""; remove_container_size = false; } }); }); resizeObserver.observe(CONTAINER); Plotly.react(PLOT, plot_obj).then(() => { // Assign the Plotly event listeners for (const [key, listener_vec] of Object.entries(plotly_listeners)) { for (const listener of listener_vec) { PLOT.on(key, listener) } } // Assign the JS event listeners for (const [key, listener_vec] of Object.entries(js_listeners)) { for (const listener of listener_vec) { PLOT.addEventListener(key, listener, { signal: controller.signal }) } } } ) invalidation.then(() => { // Remove all plotly listeners PLOT.removeAllListeners() // Remove all JS listeners controller.abort() // Remove the resizeObserver resizeObserver.disconnect() }) return CONTAINER ¤mime©text/html¬rootassigneeÀ²last_run_timestampËAÚ•ÈN…Ï°persist_js_stateÂ·has_pluto_hook_featuresÂ§cell_idÙ$b3a5adcb-5343-44e9-9466-1c51c1143a0d¹depends_on_disabled_cellsÂ§runtimeÎžƒªµpublished_object_keys‘Ù53ee53c46-38f7-11f0-16c9-2b093684d981/e3f0ea7a069278b6¸depends_on_skipped_cellsÂ§erroredÂÙ$80affd41-b5e6-4b9c-b827-4e3b39bd7767Š¦queuedÂ¤logs§runningÂ¦output†¤bodyÙ3value_iteration_v! (generic function with 1 method)¤mimeªtext/plain¬rootassigneeÀ²last_run_timestampËAÚ•Á@X°persist_js_stateÂ·has_pluto_hook_featuresÂ§cell_idÙ$80affd41-b5e6-4b9c-b827-4e3b39bd7767¹depends_on_disabled_cellsÂ§runtimeÎÌ>µpublished_object_keys¸depends_on_skipped_cellsÂ§erroredÂÙ$9a1b250f-b404-4db3-a4b7-4cd33b79d921Š¦queuedÂ¤logs§runningÂ¦output†¤bodyÙ5create_greedy_policy (generic function with 1 method)¤mimeªtext/plain¬rootassigneeÀ²last_run_timestampËAÚ•Å½<°persist_js_stateÂ·has_pluto_hook_featuresÂ§cell_idÙ$9a1b250f-b404-4db3-a4b7-4cd33b79d921¹depends_on_disabled_cellsÂ§runtimeÎ+€vµpublished_object_keys¸depends_on_skipped_cellsÂ§erroredÂÙ$db66615e-fbbc-4ea8-b529-bdc14e58a215Š¦queuedÂ¤logs§runningÂ¦output†¤bodyÚj„ ¤mime©text/html¬rootassigneeÀ²last_run_timestampËAÚ•Ê•I°persist_js_stateÂ·has_pluto_hook_featuresÂ§cell_idÙ$db66615e-fbbc-4ea8-b529-bdc14e58a215¹depends_on_disabled_cellsÂ§runtimeÎ%H^µpublished_object_keys‘Ù53ee53c46-38f7-11f0-16c9-2b093684d981/69fe4d8c476440b4¸depends_on_skipped_cellsÂ§erroredÂÙ$72b40384-9ca1-4bc1-8e1a-8b639d39e215Š¦queuedÂ¤logs§runningÂ¦output†¤bodyÚú

In this section we compare two ways of distributing updates. The classical approach, from dynamic programming, is to perform sweeps through the entire state (or state-action) space, updating each state (or state-action pair) once per sweep. This is problematic on large tasks because there may not be time to complete even one sweep. In many tasks, the vast majority or the states are irrelevant because they are visited only under very poor policies or with very low probability. Exhaustive sweeps implicitely devote equal time to all parts of the state space rather than focusing where it is needed. As we discussed in Chapter 4, exhaustive sweeps and the equal treatment of all states that they imply are not necessary properties of dynamic programming. In principle, updates can be distributed in any way one likes (to assure convergence, all states or state-action pairs must be visited in the limit an infinite number of times; although an exception to this is discussed in Section 8.7 below), but in practice exhaustive sweeps are often used.

The second approach is to sample from the state or state-action space according to some distribution. One could sample uniformly, as in the Dyna-Q agent, but this would suffer from some of the same problems as exhaustive sweeps. More appealing is to distribute updates according to the on-poliy distribution, that is according to the distribution observed when following the current policy. One advantage of this distribution is that it is easily generated; one simply interacts with the model, following the current policy. In an episodic task, one starts in a start state (or according to the starting-state distribution) and simulates until the terminal state. In a continuing task, one starts anywhere and just keeps simulating. In either case, sample state transitions and rewards are given by the model, and sample actions are given by the current policy. In other words, one simulates explicit individual trajectories and performs updates at the state or state-action pairs encountered along the way. We call this way of generating experience and updates trajectory sampling.

Is the on-policy distribution of updates a good one? Intuitively it seems like a good choice, at least better than the uniform distribution. For example, if you are learning to play chess, you study positions that might arise in real games, not random positions of chess pieces. The latter may be valid states, but to be able to accurately value them is a different skill from evaluating positions in real games. We will also see in Part II that the on policy distribution has significant advantages when function approximation is used. Whether or not function approximation is used, one might expect on-policy focusing to significantly improve the speed of planning.

The following experiment attempts to assess empirically the value of sampling from the on policy distribution by comparing it directly to the alternative of uniform sampling. In the uniform case, all state action pairs are updated once in place and in the on-policy case an episode is imulated starting from the same state and updating each state-action pair that occured under the current $\epsilon$-greedy policy $(\epsilon = 0.1)$. The tasks were undiscounted episodic tasks, generated randomly as follows. From each of the $\vert \mathcal{S} \vert$ states, two actions were possible, each of which results in one of $b$ next states, all equally likely, with a different random selection of $b$ states for each state-action pair. The branching factor, $b$, was the same for all state-action pairs. In addition, on all transitions there was a 0.1 probability of transition to the terminal state, ending the episode. The expected reward on each transition was selected from a Gaussian distribution with mean 0 and variance 1. At any point in the planning process one can stop and exhaustively compute $v_{\tilde \pi}(s_0)$, the true value of the start state under the greedy policy, $\tilde \pi$, given the current action-value function $Q$, as in indication of how well the agent would do on a new episode on which it acted greedily (all while assuming the model is correct).

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8.5 Expected vs Sample Updates

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Throughout the book we have considered a variety of value-function updates for the state value $v(s)$ and the state-action value $q(s, a)$. Broadly, they can be separated into expected updates and sample-updates. The first expected update considered was for dynamic programming in which we make use of the full probability transition function $p(s^\prime, r \vert s, a)$. When used with a policy $\pi$ that provides a probability distribution over actions for each state, this technique is called policy evaluation and when a maximization is used over actions to find optimal value function and policy policy, this corresponds to value iteration. Both of these techniques could be used with either the state or state-action value function.

The expected update for a state-action pair, $s,a$, is:

$$Q(s, a) \leftarrow \sum_{s^\prime,r}\hat p (s^\prime,r \vert s, a) \left [ \gamma \max_{a^\prime} Q(s^\prime, a^\prime) \right ] \tag{8.1}$$

Considering just one-step sample updates, we use an environment to sample a transition: $(s, a) \rightarrow (s^\prime, r)$. Since we do not use the full probability transition function, the updates make use of a learning rate and accumulate samples over time either with Sarsa or Q-learning.

The corresponding sample update for $s,a$ given a sample next state and reward, $S^\prime$ and $R$ (from the model), is the Q-learning-like update:

$$Q(s, a) \leftarrow Q(s, a) + \alpha \left [ R + \gamma \max_{a^\prime} Q(s^\prime, a^\prime) - Q(s, a) \right ] \tag{8.2}$$

where $\alpha$ is the usual positive step-size parameter.

The difference between expected and sample updates is significant to the extent that the environment is stochastic, specifically, to the extent that, given a state and action, many possible next states may occur with various probabilities. If only one next state is possible, then the expected and sample updates given above are identical (taking $\alpha=1$). If there are many possible next states, then tehre may be significant differences. In favor of the expected update is that it is an exact computation, resulting in a new $Q(s, a)$ whose correctness is limited only by the correctness of the $Q(s^\prime, a^\prime)$ at successor states. The sample update is in addition affected by sampling error. On the other hand, the sample update is cheaper computationally because it considers only one next state, not all possible next states. In practice, the computation required by update operations is usually dominated by the number of state-action pairs at which $Q$ is evaluated. For a particular starting pair, $s, a$, let $b$ be the branching factor (i.e. the number or possible next states, $s^\prime$, for which $\hat p (s^\prime, s, a) \gt 0$). Then an expected update of this pair requires roughly $b$ times as much computation as a sample update.

If there is enough time to complete an expected update, then the resulting estimate is generally better than that of $b$ sample updates because of the absense of sampling error. But if there is insufficient time to complete an expected update, then sample updates are always preferable because they at least make some improvement int he value estimate with fewer than $b$ updates. In a large problem with many state-action pairs, we are often in the latter situation. With so many state-action pairs, expected updates of all of them would take a very long time. Before that we may be much better off with a few sample updates at many state-action pairs than with expected updates at a few pairs. Given a unit of computation effort, is it better devoted to a few expected updates or to $b$ times as many sample updates?

Figure 8.7 shows the results of any analysis that suggests and answer ot this question. It shows the estimation error as a function of computation time for expected and sample updates for a variety of branching factors $b$. The case considered is that in which all $b$ successor states are equally likely and in which the error in the initial estimate is 1. The values at the next states are assumed correct, sot he expected update redues the error to zero upon its completion. In this case, sample updates reduce the error according to $\sqrt{\frac{b-1}{bt}}$ where $t$ is the number of sample updates that have been performed (assuming same averages, i.e. $\alpha = 1/t$). The key observation is that for moderately large $b$ the error falls dramatically with a tiny fraction of $b$ updates. For these cases, many state-action pairs could have their values improved dramatically, to within a few percent of the effect of an expected update, in teh same time that a single state-action pair could undergo an expected update.

The advantage of sample updates shown in Figure 8.7 is probably an underestimate of the real effect. In a real problem, the values of successor states would be estimates that are themselves updated. By causing estimates to be more accurate sooner, sample updates will have a second advantage in that the values backed up from the successor states will be more accurate. These results suggest that sample updates are likely to be superior to expected updates on problems with large stochastic branching factors and too many states to be solved exactly.

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Gridworld Visualization

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Example 8.1: Dyna Maze

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MDP Types and Functions

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Exercise 8.6

The analysis above assumed that all of the $b$ possible next states were equally likely to occur. Suppose instead that the distribution was highly skewed, that some of the $b$ states were much more likely to occur than most. Would this strengthen or weaken the case for sample updates over expected updates? Support your answer.

For the expected updates, all transitions no matter how small the probability are given the same amount of computation time. If certain transitions are much more likely than others, than the resulting state value computation is dominated by those transitions. If we perform sample updates, those transitions will appear more frequently than the less likely ones and will contribute earlier to the error reduction. In the extreme case if just one transition is close to probability 1, then that will be sampled almost every time and after just a single sample the error could be reduced close to 0. An expected update, on the other hand, would have to consider every state action pair to just perform a single update. Therefore the efficiency of sample updates would become exaggerated in all real world problems where the subsequent state transition probabilities deviate from a uniform distribution.

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Planning Steps:

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Exercise 8.3

Careful inspection of Figure 8.5 reveals that the difference between Dyna-Q+ and Dyna-Q narrowed slightly over the first part of the experiment. What is the reason for this?

As long as the environment isn't changing, no matter how small Ïµ is both algorithms will converge to the optimal policy as long as $\alpha$, $\kappa$, and $\tau$ converge to 0. For a given value of $\epsilon$, the Dyna-Q+ agent will take more exploratory actions due to the modified rewards updating the Q function. If both agents have converged to close to the optimal policy at this point, then the agent which follows a more expoitative policy will produce higher rewards. In this case, the extra exploration of Dyna-Q+ is not beneficial and its relative performance to Dyna-Q degrades.

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Actions

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Dependencies

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Example 8.3: Shortcut Maze

The problem of an incorrect model can be even worse in the case of an improvement to the environment. The following example illustrates the problem in which a maze is at first blocked forcing a longer path. After a certain number of timesteps a shorter path is opened up without disturbing the original path. The Dyna-Q agent however never learns to switch to the shortcut because the model never reveals it or provides incentive to explore paths that it already thinks are suboptimal. Even with an Ïµ-greedy policy, it is very unlikely that hte agent will take so many exploratory actions as to discover the shortcut.

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8.11 Monte Carlo Tree Search

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The general problem here is the conflict between exploration and exploitation. In a planning context, exploration means trying actions that improve the model, whereas exploitation means behaving in the optimal way given the current model. We want the agent to explore to find changes in the environment, but not so much that performance is greatly degraded. As in the earlier exploration/exploitation conflict, there probably is no solution that is both perfect and practical, but simple heuristics are often effective.

An example of such a heuristic is that used by the Dyna-Q+ agent which keeps track of how many time steps have elapsed since the state-action pair was last tried in a real interaction with the environment. The more time that has elapsed, the greater (we might presume) the chance that the dynamics of this pair has changed and that the model is incorrect. To encourage behavior that tests long-untried actions, a special "bonus reward" is given on simulated experiences involving these actions. In particular, if the modeled reward for a transition is $r$, and the transition has not been tried in $\tau$ time steps, then planning updates are done as if that transition produced a reward of $r + \kappa \sqrt{\rho}$, for some small $\kappa$. This encourages the agent to keep testing all accessible state transitions and even to find long sequences of actions in order to carry out such tests. Of course all this testing has its cost, but in many cases, as in the shortcut maze, this kind of computational curiosity is well worth the extra exploration.

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Example 8.4: Prioritized Sweeping on Mazes

Consider different mazes with the same wall structure as the Dyna Maze example but scaled up to different resolutions. Some example mazes are shown below.

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[33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 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1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck 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Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m«TransducersÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck 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Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m§PlutoUIÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] [39m[92m+ DefineSingletons v0.1.2[39m [90m[8bb1440f] [39m[92m+ DelimitedFiles v1.9.1[39m [90m[ffbed154] [39m[92m+ DocStringExtensions v0.9.4[39m [90m[4e289a0a] [39m[92m+ EnumX v1.0.5[39m [90m[e2ba6199] [39m[92m+ ExprTools v0.1.10[39m [90m[53c48c17] [39m[92m+ FixedPointNumbers v0.8.5[39m [90m[47d2ed2b] [39m[92m+ Hyperscript v0.0.5[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [90m[b5f81e59] [39m[92m+ IOCapture v0.2.5[39m [90m[22cec73e] [39m[92m+ InitialValues v0.3.1[39m [90m[842dd82b] [39m[92m+ InlineStrings v1.4.3[39m [90m[3587e190] [39m[92m+ InverseFunctions v0.1.17[39m [90m[92d709cd] [39m[92m+ IrrationalConstants v0.2.4[39m [90m[82899510] [39m[92m+ IteratorInterfaceExtensions v1.0.0[39m [90m[692b3bcd] [39m[92m+ JLLWrappers v1.7.0[39m [90m[682c06a0] [39m[92m+ JSON v0.21.4[39m [90m[b964fa9f] [39m[92m+ LaTeXStrings v1.4.0[39m [90m[2ab3a3ac] [39m[92m+ LogExpFunctions v0.3.29[39m [90m[6c6e2e6c] [39m[92m+ MIMEs v1.1.0[39m [90m[1914dd2f] [39m[92m+ MacroTools v0.5.16[39m [90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m¦RandomÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] 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[39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m¥ArrowÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] 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[90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39mSerializationÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] [39m[92m+ DefineSingletons v0.1.2[39m [90m[8bb1440f] [39m[92m+ DelimitedFiles v1.9.1[39m [90m[ffbed154] [39m[92m+ DocStringExtensions v0.9.4[39m [90m[4e289a0a] [39m[92m+ EnumX v1.0.5[39m [90m[e2ba6199] [39m[92m+ ExprTools v0.1.10[39m [90m[53c48c17] [39m[92m+ FixedPointNumbers v0.8.5[39m [90m[47d2ed2b] [39m[92m+ Hyperscript v0.0.5[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [90m[b5f81e59] [39m[92m+ IOCapture v0.2.5[39m [90m[22cec73e] [39m[92m+ InitialValues v0.3.1[39m [90m[842dd82b] [39m[92m+ InlineStrings v1.4.3[39m [90m[3587e190] [39m[92m+ InverseFunctions v0.1.17[39m [90m[92d709cd] [39m[92m+ IrrationalConstants v0.2.4[39m [90m[82899510] [39m[92m+ IteratorInterfaceExtensions v1.0.0[39m [90m[692b3bcd] [39m[92m+ JLLWrappers v1.7.0[39m [90m[682c06a0] [39m[92m+ JSON v0.21.4[39m [90m[b964fa9f] [39m[92m+ LaTeXStrings v1.4.0[39m [90m[2ab3a3ac] [39m[92m+ LogExpFunctions v0.3.29[39m [90m[6c6e2e6c] [39m[92m+ MIMEs v1.1.0[39m [90m[1914dd2f] [39m[92m+ MacroTools v0.5.16[39m [90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m©StatsBaseÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] [39m[92m+ DefineSingletons v0.1.2[39m [90m[8bb1440f] [39m[92m+ DelimitedFiles v1.9.1[39m [90m[ffbed154] [39m[92m+ DocStringExtensions v0.9.4[39m [90m[4e289a0a] [39m[92m+ EnumX v1.0.5[39m [90m[e2ba6199] [39m[92m+ ExprTools v0.1.10[39m [90m[53c48c17] [39m[92m+ FixedPointNumbers v0.8.5[39m [90m[47d2ed2b] [39m[92m+ Hyperscript v0.0.5[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [90m[b5f81e59] [39m[92m+ IOCapture v0.2.5[39m [90m[22cec73e] [39m[92m+ InitialValues v0.3.1[39m [90m[842dd82b] [39m[92m+ InlineStrings v1.4.3[39m [90m[3587e190] [39m[92m+ InverseFunctions v0.1.17[39m [90m[92d709cd] [39m[92m+ IrrationalConstants v0.2.4[39m [90m[82899510] [39m[92m+ IteratorInterfaceExtensions v1.0.0[39m [90m[692b3bcd] [39m[92m+ JLLWrappers v1.7.0[39m [90m[682c06a0] [39m[92m+ JSON v0.21.4[39m [90m[b964fa9f] [39m[92m+ LaTeXStrings v1.4.0[39m [90m[2ab3a3ac] [39m[92m+ LogExpFunctions v0.3.29[39m [90m[6c6e2e6c] [39m[92m+ MIMEs v1.1.0[39m [90m[1914dd2f] [39m[92m+ MacroTools v0.5.16[39m [90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m®DataStructuresÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] [39m[92m+ DefineSingletons v0.1.2[39m [90m[8bb1440f] [39m[92m+ DelimitedFiles v1.9.1[39m [90m[ffbed154] [39m[92m+ DocStringExtensions v0.9.4[39m [90m[4e289a0a] [39m[92m+ EnumX v1.0.5[39m [90m[e2ba6199] [39m[92m+ ExprTools v0.1.10[39m [90m[53c48c17] [39m[92m+ FixedPointNumbers v0.8.5[39m [90m[47d2ed2b] [39m[92m+ Hyperscript v0.0.5[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [90m[b5f81e59] [39m[92m+ IOCapture v0.2.5[39m [90m[22cec73e] [39m[92m+ InitialValues v0.3.1[39m [90m[842dd82b] [39m[92m+ InlineStrings v1.4.3[39m [90m[3587e190] [39m[92m+ InverseFunctions v0.1.17[39m [90m[92d709cd] [39m[92m+ IrrationalConstants v0.2.4[39m [90m[82899510] [39m[92m+ IteratorInterfaceExtensions v1.0.0[39m [90m[692b3bcd] [39m[92m+ JLLWrappers v1.7.0[39m [90m[682c06a0] [39m[92m+ JSON v0.21.4[39m [90m[b964fa9f] [39m[92m+ LaTeXStrings v1.4.0[39m [90m[2ab3a3ac] [39m[92m+ LogExpFunctions v0.3.29[39m [90m[6c6e2e6c] [39m[92m+ MIMEs v1.1.0[39m [90m[1914dd2f] [39m[92m+ MacroTools v0.5.16[39m [90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m¬StaticArraysÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] [39m[92m+ DefineSingletons v0.1.2[39m [90m[8bb1440f] [39m[92m+ DelimitedFiles v1.9.1[39m [90m[ffbed154] [39m[92m+ DocStringExtensions v0.9.4[39m [90m[4e289a0a] [39m[92m+ EnumX v1.0.5[39m [90m[e2ba6199] [39m[92m+ ExprTools v0.1.10[39m [90m[53c48c17] [39m[92m+ FixedPointNumbers v0.8.5[39m [90m[47d2ed2b] [39m[92m+ Hyperscript v0.0.5[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [90m[b5f81e59] [39m[92m+ IOCapture v0.2.5[39m [90m[22cec73e] [39m[92m+ InitialValues v0.3.1[39m [90m[842dd82b] [39m[92m+ InlineStrings v1.4.3[39m [90m[3587e190] [39m[92m+ InverseFunctions v0.1.17[39m [90m[92d709cd] [39m[92m+ IrrationalConstants v0.2.4[39m [90m[82899510] [39m[92m+ IteratorInterfaceExtensions v1.0.0[39m [90m[692b3bcd] [39m[92m+ JLLWrappers v1.7.0[39m [90m[682c06a0] [39m[92m+ JSON v0.21.4[39m [90m[b964fa9f] [39m[92m+ LaTeXStrings v1.4.0[39m [90m[2ab3a3ac] [39m[92m+ LogExpFunctions v0.3.29[39m [90m[6c6e2e6c] [39m[92m+ MIMEs v1.1.0[39m [90m[1914dd2f] [39m[92m+ MacroTools v0.5.16[39m [90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39mªnbpkg_syncÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] [39m[92m+ DefineSingletons v0.1.2[39m [90m[8bb1440f] [39m[92m+ DelimitedFiles v1.9.1[39m [90m[ffbed154] [39m[92m+ DocStringExtensions v0.9.4[39m [90m[4e289a0a] [39m[92m+ EnumX v1.0.5[39m [90m[e2ba6199] [39m[92m+ ExprTools v0.1.10[39m [90m[53c48c17] [39m[92m+ FixedPointNumbers v0.8.5[39m [90m[47d2ed2b] [39m[92m+ Hyperscript v0.0.5[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [90m[b5f81e59] [39m[92m+ IOCapture v0.2.5[39m [90m[22cec73e] [39m[92m+ InitialValues v0.3.1[39m [90m[842dd82b] [39m[92m+ InlineStrings v1.4.3[39m [90m[3587e190] [39m[92m+ InverseFunctions v0.1.17[39m [90m[92d709cd] [39m[92m+ IrrationalConstants v0.2.4[39m [90m[82899510] [39m[92m+ IteratorInterfaceExtensions v1.0.0[39m [90m[692b3bcd] [39m[92m+ JLLWrappers v1.7.0[39m [90m[682c06a0] [39m[92m+ JSON v0.21.4[39m [90m[b964fa9f] [39m[92m+ LaTeXStrings v1.4.0[39m [90m[2ab3a3ac] [39m[92m+ LogExpFunctions v0.3.29[39m [90m[6c6e2e6c] [39m[92m+ MIMEs v1.1.0[39m [90m[1914dd2f] [39m[92m+ MacroTools v0.5.16[39m [90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m°HypertextLiteralÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Removing Manifest.toml file and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Project.toml` [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[9a3f8284] [39m[93m~ Random â‡’ v1.11.0[39m [90m[9e88b42a] [39m[93m~ Serialization â‡’ v1.11.0[39m [32m[1m Updating[22m[39m `/tmp/jl_0VVRlI/Manifest.toml` [90m[6e696c72] [39m[92m+ AbstractPlutoDingetjes v1.3.2[39m [90m[7d9f7c33] [39m[92m+ Accessors v0.1.42[39m [90m[66dad0bd] [39m[92m+ AliasTables v1.1.3[39m [90m[dce04be8] [39m[92m+ ArgCheck v2.5.0[39m [33mâŒ…[39m [90m[69666777] [39m[92m+ Arrow v2.7.4[39m [90m[31f734f8] [39m[92m+ ArrowTypes v2.3.0[39m [90m[198e06fe] [39m[92m+ BangBang v0.4.4[39m [90m[18cc8868] [39m[92m+ BaseDirs v1.3.0[39m [90m[9718e550] [39m[92m+ Baselet v0.1.1[39m [90m[c3b6d118] [39m[92m+ BitIntegers v0.3.5[39m [90m[5ba52731] [39m[92m+ CodecLz4 v0.4.6[39m [90m[6b39b394] [39m[92m+ CodecZstd v0.8.6[39m [90m[35d6a980] [39m[92m+ ColorSchemes v3.29.0[39m [33mâŒ…[39m [90m[3da002f7] [39m[92m+ ColorTypes v0.11.5[39m [32mâŒƒ[39m [90m[c3611d14] [39m[92m+ ColorVectorSpace v0.10.0[39m [33mâŒ…[39m [90m[5ae59095] [39m[92m+ Colors v0.12.11[39m [90m[34da2185] [39m[92m+ Compat v4.16.0[39m [90m[a33af91c] [39m[92m+ CompositionsBase v0.1.2[39m [90m[f0e56b4a] [39m[92m+ ConcurrentUtilities v2.5.0[39m [90m[187b0558] [39m[92m+ ConstructionBase v1.5.8[39m [90m[9a962f9c] [39m[92m+ DataAPI v1.16.0[39m [90m[864edb3b] [39m[92m+ DataStructures v0.18.22[39m [90m[e2d170a0] [39m[92m+ DataValueInterfaces v1.0.0[39m [90m[244e2a9f] [39m[92m+ DefineSingletons v0.1.2[39m [90m[8bb1440f] [39m[92m+ DelimitedFiles v1.9.1[39m [90m[ffbed154] [39m[92m+ DocStringExtensions v0.9.4[39m [90m[4e289a0a] [39m[92m+ EnumX v1.0.5[39m [90m[e2ba6199] [39m[92m+ ExprTools v0.1.10[39m [90m[53c48c17] [39m[92m+ FixedPointNumbers v0.8.5[39m [90m[47d2ed2b] [39m[92m+ Hyperscript v0.0.5[39m [90m[ac1192a8] [39m[92m+ HypertextLiteral v0.9.5[39m [90m[b5f81e59] [39m[92m+ IOCapture v0.2.5[39m [90m[22cec73e] [39m[92m+ InitialValues v0.3.1[39m [90m[842dd82b] [39m[92m+ InlineStrings v1.4.3[39m [90m[3587e190] [39m[92m+ InverseFunctions v0.1.17[39m [90m[92d709cd] [39m[92m+ IrrationalConstants v0.2.4[39m [90m[82899510] [39m[92m+ IteratorInterfaceExtensions v1.0.0[39m [90m[692b3bcd] [39m[92m+ JLLWrappers v1.7.0[39m [90m[682c06a0] [39m[92m+ JSON v0.21.4[39m [90m[b964fa9f] [39m[92m+ LaTeXStrings v1.4.0[39m [90m[2ab3a3ac] [39m[92m+ LogExpFunctions v0.3.29[39m [90m[6c6e2e6c] [39m[92m+ MIMEs v1.1.0[39m [90m[1914dd2f] [39m[92m+ MacroTools v0.5.16[39m [90m[128add7d] [39m[92m+ MicroCollections v0.2.0[39m [90m[e1d29d7a] [39m[92m+ Missings v1.2.0[39m [90m[78c3b35d] [39m[92m+ Mocking v0.8.1[39m [90m[bac558e1] [39m[92m+ OrderedCollections v1.8.1[39m [90m[d96e819e] [39m[92m+ Parameters v0.12.3[39m [90m[69de0a69] [39m[92m+ Parsers v2.8.3[39m [90m[a03496cd] [39m[92m+ PlotlyBase v0.8.21[39m [33mâŒ…[39m [90m[8e989ff0] [39m[92m+ PlutoPlotly v0.4.6[39m [90m[7f904dfe] [39m[92m+ PlutoUI v0.7.62[39m [90m[2dfb63ee] [39m[92m+ PooledArrays v1.4.3[39m [33mâŒ…[39m [90m[aea7be01] [39m[92m+ PrecompileTools v1.2.1[39m [90m[21216c6a] [39m[92m+ Preferences v1.4.3[39m [90m[43287f4e] [39m[92m+ PtrArrays v1.3.0[39m [90m[189a3867] [39m[92m+ Reexport v1.2.2[39m [90m[ae029012] [39m[92m+ Requires v1.3.1[39m [90m[6c6a2e73] [39m[92m+ Scratch v1.2.1[39m [90m[91c51154] [39m[92m+ SentinelArrays v1.4.8[39m [90m[efcf1570] [39m[92m+ Setfield v1.1.2[39m [90m[a2af1166] [39m[92m+ SortingAlgorithms v1.2.1[39m [90m[171d559e] [39m[92m+ SplittablesBase v0.1.15[39m [90m[90137ffa] [39m[92m+ StaticArrays v1.9.13[39m [90m[1e83bf80] [39m[92m+ StaticArraysCore v1.4.3[39m [90m[10745b16] [39m[92m+ Statistics v1.11.1[39m [90m[82ae8749] [39m[92m+ StatsAPI v1.7.0[39m [90m[2913bbd2] [39m[92m+ StatsBase v0.34.5[39m [90m[dc5dba14] [39m[92m+ TZJData v1.5.0+2025b[39m [90m[3783bdb8] [39m[92m+ TableTraits v1.0.1[39m [90m[bd369af6] [39m[92m+ Tables v1.12.0[39m [90m[62fd8b95] [39m[92m+ TensorCore v0.1.1[39m [90m[f269a46b] [39m[92m+ TimeZones v1.21.3[39m [90m[3bb67fe8] [39m[92m+ TranscodingStreams v0.11.3[39m [90m[28d57a85] [39m[92m+ Transducers v0.4.84[39m [90m[410a4b4d] [39m[92m+ Tricks v0.1.10[39m [90m[5c2747f8] [39m[92m+ URIs v1.5.2[39m [90m[3a884ed6] [39m[92m+ UnPack v1.0.2[39m [90m[5ced341a] [39m[92m+ Lz4_jll v1.10.1+0[39m [90m[3161d3a3] [39m[92m+ Zstd_jll v1.5.7+1[39m [90m[0dad84c5] [39m[92m+ ArgTools v1.1.2[39m [90m[56f22d72] [39m[92m+ Artifacts v1.11.0[39m [90m[2a0f44e3] [39m[92m+ Base64 v1.11.0[39m [90m[ade2ca70] [39m[92m+ Dates v1.11.0[39m [90m[8ba89e20] [39m[92m+ Distributed v1.11.0[39m [90m[f43a241f] [39m[92m+ Downloads v1.6.0[39m [90m[7b1f6079] [39m[92m+ FileWatching v1.11.0[39m [90m[9fa8497b] [39m[92m+ Future v1.11.0[39m [90m[b77e0a4c] [39m[92m+ InteractiveUtils v1.11.0[39m [90m[b27032c2] [39m[92m+ LibCURL v0.6.4[39m [90m[76f85450] [39m[92m+ LibGit2 v1.11.0[39m [90m[8f399da3] [39m[92m+ Libdl v1.11.0[39m [90m[37e2e46d] [39m[92m+ LinearAlgebra v1.11.0[39m [90m[56ddb016] [39m[92m+ Logging v1.11.0[39m [90m[d6f4376e] [39m[92m+ Markdown v1.11.0[39m [90m[a63ad114] [39m[92m+ Mmap v1.11.0[39m [90m[ca575930] [39m[92m+ NetworkOptions v1.2.0[39m [90m[44cfe95a] [39m[92m+ Pkg v1.11.0[39m [90m[de0858da] [39m[92m+ Printf v1.11.0[39m [90m[3fa0cd96] [39m[92m+ REPL v1.11.0[39m [90m[9a3f8284] [39m[92m+ Random v1.11.0[39m [90m[ea8e919c] [39m[92m+ SHA v0.7.0[39m [90m[9e88b42a] [39m[92m+ Serialization v1.11.0[39m [90m[6462fe0b] [39m[92m+ Sockets v1.11.0[39m [90m[2f01184e] [39m[92m+ SparseArrays v1.11.0[39m [90m[f489334b] [39m[92m+ StyledStrings v1.11.0[39m [90m[fa267f1f] [39m[92m+ TOML v1.0.3[39m [90m[a4e569a6] [39m[92m+ Tar v1.10.0[39m [90m[8dfed614] [39m[92m+ Test v1.11.0[39m [90m[cf7118a7] [39m[92m+ UUIDs v1.11.0[39m [90m[4ec0a83e] [39m[92m+ Unicode v1.11.0[39m [90m[e66e0078] [39m[92m+ CompilerSupportLibraries_jll v1.1.1+0[39m [90m[deac9b47] [39m[92m+ LibCURL_jll v8.6.0+0[39m [90m[e37daf67] [39m[92m+ LibGit2_jll v1.7.2+0[39m [90m[29816b5a] [39m[92m+ LibSSH2_jll v1.11.0+1[39m [90m[c8ffd9c3] [39m[92m+ MbedTLS_jll v2.28.6+0[39m [90m[14a3606d] [39m[92m+ MozillaCACerts_jll v2023.12.12[39m [90m[4536629a] [39m[92m+ OpenBLAS_jll v0.3.27+1[39m [90m[bea87d4a] [39m[92m+ SuiteSparse_jll v7.7.0+0[39m [90m[83775a58] [39m[92m+ Zlib_jll v1.2.13+1[39m [90m[8e850b90] [39m[92m+ libblastrampoline_jll v5.11.0+0[39m [90m[8e850ede] [39m[92m+ nghttp2_jll v1.59.0+0[39m [90m[3f19e933] [39m[92m+ p7zip_jll v17.4.0+2[39m [36m[1m Info[22m[39m Packages marked with [32mâŒƒ[39m and [33mâŒ…[39m have new versions available. Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m«PlutoPlotlyÚA [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Fixing stdlib dependencies and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mStrategy failed to run. [33m[1mâ”‚ [22m[39m exception = [33m[1mâ”‚ [22m[39m The following package names could not be resolved: [33m[1mâ”‚ [22m[39m * Statistics (not found in project or manifest) [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:98[39m [0m[1mResolving...[22m [90m===[39m [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. Updating registries and trying again... [33m[1mâ”” [22m[39m[90m@ GracefulPkg ~/.julia/packages/GracefulPkg/iMkQe/src/apply strategies.jl:92[39m [0m[1mResolving...[22m [90m===[39m [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Arrow" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StaticArrays" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "StatsBase" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "DataStructures" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "Transducers" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoUI" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "HypertextLiteral" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [36m[1mâ”Œ [22m[39m[36m[1mInfo: [22m[39mChecking [36m[1mâ”‚ [22m[39m package_name = "PlutoPlotly" [36m[1mâ”” [22m[39m error_str = "Pkg.Resolve.ResolverError(\"Unsatisfiable requirements detected for package Statistics [10745b16]:\\n Statistics [10745b16] log:\\n â”œâ”€possible versions are: 1.11.0 - 1.11.1 or uninstalled\\n â”œâ”€restricted to versions * by project [34115c3b], leaving only ver"[93m[1m â‹¯ 1595 bytes â‹¯ [22m[39m" versions: 1.11.0 - 1.11.1\\n â”‚ â””â”€project [34115c3b] log:\\n â”‚ â”œâ”€possible versions are: 0.0.0 or uninstalled\\n â”‚ â””â”€project [34115c3b] is fixed to version 0.0.0\\n â””â”€restricted to versions 1.10.0 by an explicit requirement â€” no versions left\", nothing)" [33m[1mâ”Œ [22m[39m[33m[1mWarning: [22m[39mPkg operation failed. 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Those with [32mâŒƒ[39m may be upgradable, but those with [33mâŒ…[39m are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m [32m[1m Activating[22m[39m project at `/tmp/jl_0VVRlI` [92m[1mPrecompiling[22m[39m project... 4 dependencies successfully precompiled in 9 seconds. 127 already precompiled. [33m1[39m dependency had output during precompilation:[33m â”Œ [39mPlutoPlotly[33m â”‚ [39mâ”Œ Warning: A base directory is being computed during precompilation.[33m â”‚ [39mâ”‚ This is dangerous, as results depend on the live system configuration.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ It is recommended that you invoke BaseDirs as required in[33m â”‚ [39mâ”‚ function bodies, rather than at the top level. Calls are very[33m â”‚ [39mâ”‚ cheap, and you can always pass the result of a live call around.[33m â”‚ [39mâ”‚ [33m â”‚ [39mâ”‚ If you have verified that this call was not assigned to a global constant,[33m â”‚ [39mâ”‚ you can silence this warning with `BaseDirs.@promise_no_assign`.[33m â”‚ [39mâ”” @ BaseDirs ~/.julia/packages/PlutoPlotly/PecJP/src/local_plotly_library.jl:1[33m â”” [39m§enabledÃ·restart_recommended_msgÀ´restart_required_msgÀbusy_packages¶waiting_for_permissionÂÙ,waiting_for_permission_but_probably_disabledÂ«cell_inputsÞ Ù$2fa207dd-749f-4dc0-b4ab-159edf1d9bce„§cell_idÙ$2fa207dd-749f-4dc0-b4ab-159edf1d9bce¤codeÙ¾begin_value_iteration_v(mdp::CompleteMDP{T,S,A}, Î³::T; Vinit::T = zero(T), kwargs...) where {T<:Real,S,A} = begin_value_iteration_v(mdp, Î³, Vinit .* ones(T, length(mdp.states)); kwargs...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$e0cc1ca1-595d-44e2-8612-261df9e2d327„§cell_idÙ$e0cc1ca1-595d-44e2-8612-261df9e2d327¤codeÚÏmd""" > ### *Exercise 8.1* > The nonplanning method looks particularly poor in Figure 8.3 because it is a one-step method; a method using multi-step bootstrapping would do better. Do you think one of the multi-step bootstrapping methods from Chapter 7 could do as well as the Dyna method? Explain why or why not. For the n = 50 agent, it can learn a policy that covers nearly the entire maze during the second episode. In the extreme case of a multistep method we would attempt to solve the maze using monte carlo sampling in which case after a single episode we would have action/value updates for every state visited along the random trajectory. However, these action/value estimates would be high variance estimates of the randomly initialized policy. In contrast, the Dyna method after one random episode has observed most or all of the transitions in the maze. During the long planning phase its Q updates would actually be able to converge to the optimal policy given a large enough n using bootstrapping and the single reward from reaching the goal. As long as something is sampled close to the goal that information will propagate through to the rest of the states and each update is simultaneously improving the Ïµ-greedy policy. With multi-step bootstrapping we can extend the updates back along the trajectory a certain distance, but in the extreme case we just sample values from the random policy without any bootstrapping or we bootstrap to a limited degree close to the goal and still have the lack of information further away from it. Since this environment is deterministic, having the sample transitions is equivalent to having a full model of the environment. This could be used explicitely with value iteration to obtain the Q function as well. The planning step is effectively performing this computation but only using the known transitions and over time is focused only on the states visited by the optimal or nearly optimal policy. No n-step method can take advantage of previously observed transitions this well, but it should be noted that Dyna-Q only works this well if the environment is deterministic and is taking advantage of the fact that we know our model is correct for the transitions already observed. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$304e6afd-11e0-4011-9929-85889b988400„§cell_idÙ$304e6afd-11e0-4011-9929-85889b988400¤codeÚWstruct MDP_MC{S, A, F<:Function, G<:Function, H<:Function} <: MDP{S, A, F, G, H} init_states::Vector{S} actions::Vector{A} step::F #function that produces reward and updated state given a state action pair state_init::G #function that produces an initial state for an episode isterm::H #function that returns true if the state input is terminal function MDP_MC(init_states::Vector{S}, actions::Vector{A}, step::F, isterm::H) where {S, A, F<:Function, H<:Function} state_init() = rand(init_states) new{S, A, F, typeof(state_init), H}(init_states, actions, step, state_init, isterm) end end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$96bd8d33-d4e8-45bf-9b75-43e8bda6fa07„§cell_idÙ$96bd8d33-d4e8-45bf-9b75-43e8bda6fa07¤codeÚšfunction make_Ïµ_greedy_policy!(v::AbstractVector{T}, Ïµ::T; valid_inds = eachindex(v)) where T <: Real vmax = maximum(view(v, valid_inds)) v .= T.(isapprox.(v, vmax)) s = sum(v) c = Ïµ / length(valid_inds) d = c + (one(T) - Ïµ)/s #value to add to actions that are maximizing v .= c .+ (v .* d) # for i in valid_inds # if v[i] == 1 # v[i] = d + c # else # v[i] = c # end # end return v end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$3b4e27e7-8065-44b3-bc2a-e540913aa540„§cell_idÙ$3b4e27e7-8065-44b3-bc2a-e540913aa540¤codeÚ–function takestep(mdp::FiniteStochasticMDP{T, S, A}, Ï€::Matrix{T}, s) where {T<:Real, S, A} i_s = mdp.state_index[s] i_a = sample_action(Ï€, i_s) ptf = mdp.ptf[(i_a, i_s)] probabilities = [ptf[i_sâ€²][1] for i_sâ€² in keys(ptf)] i_sâ€² = sample(collect(keys(ptf)), weights(probabilities)) sâ€² = mdp.states[i_sâ€²] r = ptf[i_sâ€²][2] a = mdp.actions[i_a] return (i_s, i_sâ€², r, sâ€², a, i_a) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6e273f2b-a1af-421f-aca7-772a836b89ef„§cell_idÙ$6e273f2b-a1af-421f-aca7-772a836b89ef¤codeÚofunction uniform_bellman_policy_value!(Q::Matrix{T}, Ï€::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, Î³::T) where {T <: Real, S, A} delt = zero(T) num_updates = 0 for i_s in eachindex(mdp.states) for i_a in eachindex(mdp.actions) delt = max(delt, bellman_policy_update!(Q, Ï€, i_s, i_a, mdp, Î³)) num_updates += 1 end end return delt, num_updates end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$726af565-8905-4409-864f-a5c1b5767e09„§cell_idÙ$726af565-8905-4409-864f-a5c1b5767e09¤codeÚ`#forms a random policy for a generic finite state mdp. The policy is a matrix where the rows represent actions and the columns represent states. Each column is a probability distribution of actions over that state. form_random_policy(mdp::CompleteMDP{T, S, A}) where {T, S, A} = ones(T, length(mdp.actions), length(mdp.states)) ./ length(mdp.actions)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$5d2abde0-7128-41c3-bd1f-b6940492d1ae„§cell_idÙ$5d2abde0-7128-41c3-bd1f-b6940492d1ae¤codeÚ¿function double_q_learning(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, qinit = zero(T), Ïµinit = one(T)/10, Qinit::Matrix{T} = initialize_state_action_value(mdp; qinit=qinit), decay_Ïµ = false, target_policy_function! = (v, Ïµ, s) -> make_greedy_policy!(v), behavior_policy_function! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), Ï€init_target::Matrix{T} = create_greedy_policy(Qinit), Ï€init_behavior::Matrix{T} = create_Ïµ_greedy_policy(Qinit, Ïµinit), save_state::S = first(mdp.states), save_history = false) where {S, A, F, G, H, T<:AbstractFloat} double_expected_sarsa(mdp, Î±, Î³; num_episodes = num_episodes, qinit = qinit, Ïµinit = Ïµinit, Qinit = Qinit, decay_Ïµ = decay_Ïµ, target_policy_function! = target_policy_function!, behavior_policy_function! = behavior_policy_function!, Ï€init_target = Ï€init_target, Ï€init_behavior = Ï€init_behavior, save_state = save_state, save_history = save_history) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$76489f21-677e-4c25-beaa-afaf2244cd94„§cell_idÙ$76489f21-677e-4c25-beaa-afaf2244cd94¤codeÙImd""" #### Figure 8.2 Learning curves for a Dyna-Q agent in the maze """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$1127c36f-9bc0-49b1-9481-8a5861bdf6ca„§cell_idÙ$1127c36f-9bc0-49b1-9481-8a5861bdf6ca¤codeÚ 7md""" > ### *Exercise 8.5* > How might the tabular Dyna-Q algorithm shown above be modified to handle stochastic environments? How might this modification perform poorly on changing environments such as considered in this section? How could the algorithm be modified to handle stochastic environments *and* changing environments? Instead of storing the most recent transition for a state-action pair, we could store the fraction of the time different transition states and rewards are observed. That way our model becomes a distribution model using the previously experienced samples as estimates of the true transition probabilities. Then for the planning updates, we would sample the transition from the model stochastically and perform the same Q update. If the environment is changing, then the model will take much longer to modify compared to the original version where as soon as a new transition is observed it replaces the previous value. If we averaging the previously observed transitions and there are many actions, then a new experience would only slightly modify the model even if we use constant step size averaging. We would therefore expect such a model to react much more slowly to a changing environment. One solution to speed up model updates could be to use a modified reward such as what is used for Dyna-Q+ in which the time since a state-action pair was selected increases the planning reward update for that transition, but that method assumed that a single visit to the state is adequate to update the model. In a stochastic environment if we are counting transition to have an estimate of the probabilities for each one, our method would need a way of forgetting transitions that no longer happen. We could simply keep only data from the most recent n transitions or average with exponential decaying weights such that we give higher weight to more cently observed transitions in the probability distribution. We may also have to keep track of the time since a specific transitions was seen rather than simply the time a state action pair was attempted, so we'd have $\tau(s, a, sâ€², r)$ and then the planning Q update could add up or take the maximum of all of the observed transitions from that state. A state action pair would then be boosted if any one of the possible transitions has not been seen recently. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$69ff1b72-cb1b-4724-a445-38e4c9846964„§cell_idÙ$69ff1b72-cb1b-4724-a445-38e4c9846964¤codeÚ>md""" #### Figure 8.4: Average performance of Dyna-Q agent on a blocking task. After 1000 episodes of training the environment is switched to the second version. The flat portion of the graph shows the agent getting stuck following the old optimal policy and needing many time steps to learn the new environment """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$3edc09ab-8fa1-440f-8a45-546898a2b2a3„§cell_idÙ$3edc09ab-8fa1-440f-8a45-546898a2b2a3¤codeÙì#take a step in the environment from state s using policy Ï€ represented by a function that returns an action given a state function takestep(mdp, Ï€::Function, s) a = Ï€(s) (r, sâ€²) = mdp.step(s, a) return (0, 0, r, sâ€², a, 0) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6c8cd429-2c2e-4515-98b2-d0394962e479„§cell_idÙ$6c8cd429-2c2e-4515-98b2-d0394962e479¤codeÚIfunction takestep(mdp::FiniteDeterministicMDP{T, S, A}, Ï€::Matrix{T}, s) where {T<:Real, S, A} i_s = mdp.state_index[s] i_a = sample_action(Ï€, i_s) i_sâ€² = mdp.state_transition_map[i_a, i_s] r = mdp.reward_transition_map[i_a, i_s] sâ€² = mdp.states[i_sâ€²] a = mdp.actions[i_a] return (i_s, i_sâ€², r, sâ€², a, i_a) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6605b946-3010-47ed-8d88-3c4dca993cf8„§cell_idÙ$6605b946-3010-47ed-8d88-3c4dca993cf8¤codeÙÓbegin_value_iteration_q(mdp::CompleteMDP{T,S,A}, Î³::T; Qinit::T = zero(T), kwargs...) where {T<:Real,S,A} = begin_value_iteration_q(mdp, Î³, Qinit .* ones(T, length(mdp.actions), length(mdp.states)); kwargs...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$00c8f62f-dee2-476c-b896-68d3ab57a168„§cell_idÙ$00c8f62f-dee2-476c-b896-68d3ab57a168¤codeÚòfunction figure_8_8(num_states; expected_updates_max = 20_000, num_samples = 200, blist = [1, 3, 10]) function get_uniform_value(num_states, b) Q = zeros(2, num_states + 1) Qref = copy(Q) Ï€ = copy(Q) mdp = make_random_mdp(num_states, b) total_updates = 0 x = Vector{Int64}() y = Vector{Float64}() while total_updates <= expected_updates_max (delt, num_updates) = uniform_bellman_optimal_value!(Q, mdp, 1.0) # make_greedy_policy!(Ï€, mdp, Q) # q_policy_evaluation!(Qref, Ï€, mdp, 1.0) # qval = maximum(Qref[:, 1]) qval = maximum(Q[:, 1]) total_updates += num_updates push!(x, num_updates) push!(y, qval) end return x, y end function get_on_policy_value(num_states, b) Q = zeros(2, num_states + 1) Ï€ = copy(Q) mdp = make_random_mdp(num_states, b) total_updates = 0 x = Vector{Int64}() y = Vector{Float64}() while total_updates <= expected_updates_max (delt, num_updates) = trajectory_bellman_optimal_value!(Q, Ï€, mdp, 1.0, 0.1, 1, s -> s == 0) qval = maximum(Q[:, 1]) total_updates += num_updates push!(x, total_updates) push!(y, qval) end updatecounts = collect(1:num_states*2:expected_updates_max) values = [y[min(searchsortedfirst(x, c), length(y))] for c in updatecounts] return updatecounts, values end uniform_traces = [begin out = 1:num_samples |> Map(_ -> get_uniform_value(num_states, b)) |> foldxt((x, y) -> ((x[1] .+ y[1]), (x[2] .+ y[2]))) trace = scatter(x = [0; cumsum(out[1] ./ num_samples)], y = [0; out[2] ./ num_samples], name = "b = $b, uniform") end for b in blist] on_policy_traces = [begin out = 1:num_samples |> Map(_ -> get_on_policy_value(num_states, b)) |> foldxt((x, y) -> ((x[1] .+ y[1]), (x[2] .+ y[2]))) trace = scatter(x = [0; out[1] ./ num_samples], y = [0; out[2] ./ num_samples], name = "b = $b, on-policy") end for b in blist] plot([uniform_traces; on_policy_traces], Layout(xaxis_title = "Computation time, in expected updates", yaxis_title = "Value of start state under greedy policy")) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$4d4baa61-b5bd-4bcf-a491-9a35a1695f0b„§cell_idÙ$4d4baa61-b5bd-4bcf-a491-9a35a1695f0b¤code»figure8_3(num_episodes_8_3)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$cd79ce14-14a1-43c6-93e0-b4a786f7f9fb„§cell_idÙ$cd79ce14-14a1-43c6-93e0-b4a786f7f9fb¤codeÚ$md""" Because planning proceeds incrementally, it is trivial to intermix planning and acting. So the learning from the model can inform the interaction with the environment in parallel and then any information from the environment can be used to update the model whenever it is received. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$516234a8-2748-11ed-35df-432eebaa5162„§cell_idÙ$516234a8-2748-11ed-35df-432eebaa5162¤codeÚsmd""" # Chapter 8 - Planning and Learning with Tabular Methods So far we have seen example problems where we have a full model of the environment in the form of the probability transition function as well as environments where we can only sample trajectories. In this chapter we will integrate the techniques that are *model-based* and *model-free* and show how they can be intermixed. ## 8.1 Models and Planning A *model* is anything that an agent can use to predict the environment. If the model provides a full description of all possible transitions it is called a *distribution model* vs a *sample model* that can only generate one of those possibilities according to the correct probability distribution. In dynamic programming, we used a distribution model while for certain example problems such as blackjack we only had a sample model. A model can be used the create a *simulated experience* of the environment such as a trajectory. The common thread across all the techniques is the computation of the value function to improve a policy and using some update process to compute the value function for example from the data collected in simulated experience. For the learning methods considered so far, we have assumed that the data collected from trajectories is generated by the environment itself while in planning methods this experience would come instead from a model. However the learning techniques largely still apply to planning techniques as well since the nature of the data is the same. Consider a planning method analogous to Q-learning called *random-sample one-step tabular Q-planning*. This technique applies the Q-learning update to a transition sapmled from a model. Instead of interacting with the environment in an episode or continuing task, this technique simply selects a state action pair at random and observes the transition. Just like with Q-learning, this method converges to the optimal policy under the assumption that all state-action pairs are visited an infinite number of times but the policy will only be optimal for the model of the environment. Performing updates on single transitions highlights another theme of planning methods which don't necessarily involve exaustive solutions to the whole environment. We can direct the method to specific states of interest which may be important for problems with very large state spaces. ## 8.2 Dyna: Integrated Planning, Acting, and Learning A planning agent can use real experience in at least two ways: 1) it can be used to improve the model to make it a better match for the real environment (*model-learning*) and 2) it can be used directly to improve the value function using the previous learning methods (*direct reinforcement learning*). If a better model is then used to improve the value function this is also called *indirect reinforcement learning*. Indirect methods can make better use of a limited amount of experience, but direct methods are much simpler and are not affected by the biases in the design of the model. Dyna-Q includes all the processes of planning, acting, model-learning, and direct RL. The planning method is the random-sample one-step tabular Q-planning described above. The direct RL method is one-step tabular Q-learning. The model-learning mdethod is also table-based and assumes the environment is deterministic. After each transition $S_t,A_t \longrightarrow R_{t+1},S_{t+1}$, the model records in its table entry for $S_t,A_t$ the prediction that $R_{t+1},S_{t+1}$ will deterministically follow. Thus if the model is queried with a state-action pair that has been experienced before, it simply returns the last-observed next state and next reward as its prediction. During planning, the Q-planning algorithm randomly samples only from state-action pairs that have previously been experienced, so the model is never queried with a pair about which it has no information. The learning and planning portions of the algorithm are connected in that they use the same type of update. The only difference is the source of the experience used. The collection of real experience and planning could occur simultaneously in these agents, but for a serial implementation it is assumed that the acting, model-learning, and direct RL processes are very fast while the planning process is the model computation-intensive. Let us assume that after each step of acting, model-learning, and direct RL there is time for $n$ iterations of the Q-planning algorithm. Without the model update and the $n$ loop planning step, this algorithm is identical to one-step tabular Q-learning. An example implementation is below along with an example applying it to a maze environment. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$bf7950f2-05fa-4455-ad08-27735148d95c„§cell_idÙ$bf7950f2-05fa-4455-ad08-27735148d95c¤codeÚ e#perform action selection within an mdp for a given state s, discount factor Î³, and state value estimation function v_est. v_est must be a function that takes the arguments (mdp, s, Î³) and produces a reward of the same type as Î³ function monte_carlo_tree_search(mdp::AfterstateMDP_MC{S, AS, A, F, G, H, I}, Î³::T, v_est::Function, s::S; depth = 10, nsims = 100, c = one(T), tree_values = Dict{S, Tuple{T, Dict{Int64, Tuple{T, T}}}}(), update_tree_policy! = (v, s) -> make_greedy_policy!(v), v_hold = zeros(T, length(mdp.actions)), update_tree! = function(tree_values, v::T, s::S, i_a::Integer) d = tree_values[s][2] new_value = if haskey(d, i_a) (d[i_a][1]+1, d[i_a][2]+v) else (1f0, v) end tree_values[s][2][i_a] = new_value end, apply_bonus! = apply_uct!, make_step_kwargs = k -> NamedTuple(), #option to create mdp afterstate step arguments that depend on the simulation number make_transition_kwargs = k -> NamedTuple(), #option to create mdp afterstate transition arguments that depend on the simulation number make_est_kwargs = k -> NamedTuple(), #option to create state estimation arguments that depend on the simulation number sim_message = false ) where {S, AS, A, F, G, H, I, T<:Real} q_hold = zeros(T, length(mdp.actions)) #I want to have a way of possible a kwargs such as the answer index to the simulator that can change with each simulation t = time() last_time = t for k in 1:nsims seed = rand(UInt64) if sim_message elapsed = time() - last_time if elapsed > 5 last_time = time() pct_done = k/nsims total_time = time() - t ett = total_time / pct_done eta = ett - total_time @info """Completed simulation $k of $nsims after $(round(Int64, total_time/60)) minutes ETA: $(round(Int64, eta/60)) minutes""" end end simulate!(s, true, tree_values, mdp, Î³, v_est, depth, c, v_hold, update_tree_policy!, update_tree!, q_hold, apply_bonus!, make_step_kwargs(seed), make_transition_kwargs(seed), make_est_kwargs(seed)) end minv = minimum(t[2]/t[1] for t in values(tree_values[s][2])) for i in eachindex(v_hold) if haskey(tree_values[s][2], i) v_hold[i] = tree_values[s][2][i][2] / tree_values[s][2][i][1] else v_hold[i] = minv end end make_greedy_policy!(v_hold) if sim_message @info "Finished MCTS evaluation of state $s" end return mdp.actions[sample_action(v_hold)], tree_values end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$4d1f0065-e5ab-46fa-8ab1-a0bbcf523c27„§cell_idÙ$4d1f0065-e5ab-46fa-8ab1-a0bbcf523c27¤codeÙDfigure_8_4(;ntrials = 10, Ïµ = 0.1f0, Î± = 0.8f0, fig_8_4_params...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$47ce2eda-b2c4-4f81-8d91-955bc35bab49„§cell_idÙ$47ce2eda-b2c4-4f81-8d91-955bc35bab49¤codeÚ¡begin struct AfterstateMDP_MC{S, AS, A, F<:Function, G<:Function, H<:Function, I<:Function} init_states::Vector{S} init_afterstates::Dict{Tuple{S, A}, AS} actions::Vector{A} afterstate_step::F #deterministic function that produces a reward and afterstate given a state action pair afterstate_transition::G #stochastic function that preduces a probability distribution of reward, state pairs given an afterstate state_init::H #function that produces an initial state for an episode isterm::I #function that returns true if the state input is terminal action_index::Dict{A, Int64} function AfterstateMDP_MC(init_states::Vector{S}, actions::Vector{A}, afterstate_step::F, afterstate_transition::G, isterm::I) where {S, A, F<:Function, G<:Function, I<:Function} state_init() = rand(init_states) init_afterstates = Dict((s, a) => afterstate_step(s, a)[2] for a in actions for s in init_states) action_index = makelookup(actions) new{S, typeof(init_afterstates[first(init_states), first(actions)]), A, F, G, typeof(state_init), I}(init_states, init_afterstates, actions, afterstate_step, afterstate_transition, state_init, isterm, action_index) end end end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$679b6096-b3ca-422d-9d9a-225832510ab1„§cell_idÙ$679b6096-b3ca-422d-9d9a-225832510ab1¤codeÙ(make_Ïµ_greedy_policy!(vholdtest, 0.1f0)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$d5ac7c6f-9636-46d9-806f-34d6c8e4d4d5„§cell_idÙ$d5ac7c6f-9636-46d9-806f-34d6c8e4d4d5¤codeÚêmd""" Consider the simple maze shown above where the agent can take four actions which take it deterministically into the neighboring square unless it is blocked by a wall represented by a W in the diagram. In this case the agent remains in its original state. Reward is zero on all transitions except those into the goal state, on which it is +1. After reaching the goal the agent returns to the start to begin a new episode. This is a discounted episodic task with $\gamma = 0.95$ """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$aee1f6a8-de43-402e-b375-86c0f2f9e6b8„§cell_idÙ$aee1f6a8-de43-402e-b375-86c0f2f9e6b8¤codeÚwfunction figure_8_5â€²(; max_steps1 = 3000, max_steps2 = 3000, ntrials = 10, n = 50, Î± = 0.5f0, Ïµ = 0.2f0, Îº = 0.001f0) x = 1:(max_steps1 + max_steps2) function test_algo(f) y = zeros(max_steps1 + max_steps2) for _ in 1:ntrials (Q, steps, rewards, history, stm, rtm) = f(blocking_maze2, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps1, n = n) (Q2, steps2, rewards2, _, _) = f(blocking_maze3, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps2, n = n, Qinit = Q, history = history, state_transition_map = stm, reward_transition_map = rtm, init_step = max_steps1) y .+= cumsum([rewards; rewards2]) end return x, y end (x1, y1) = test_algo(tabular_dynaQ) (x2, y2) = test_algo((args...; kwargs...) -> tabular_dynaQplus(args...; Îº = Îº, kwargs...)) plot([scatter(x = x, y = y1 ./ ntrials, name = "Dyna-Q"), scatter(x = x, y = y2 ./ ntrials, name = "Dyna-Q+")]) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$a3243bc4-7ae7-418a-9881-a265ca95f5ef„§cell_idÙ$a3243bc4-7ae7-418a-9881-a265ca95f5ef¤codeÙ%md""" ## 8.4 Prioritized Sweeping """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$466268c4-664d-42dd-84c1-7b8ade49936f„§cell_idÙ$466268c4-664d-42dd-84c1-7b8ade49936f¤codeÚ©md""" For each maze there is an optimal path that scales up with the size. The plot below compares prioritized sweeping with Dyna-Q where each method can make at most 5 planning updates. The number of updates until an optimal path is found is computed for different size mazes. The number of updates required for prioritied sweeping is dramatically lower by tens or hundreds of times depending on the size of the maze. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$f9cdd5a8-3a9b-4be4-9a33-bb0047ac4a96„§cell_idÙ$f9cdd5a8-3a9b-4be4-9a33-bb0047ac4a96¤codeÙ±#create a function that performs a rollout given an mdp, starting state, and discount rate rollout(;max_steps = Inf) = (mdp, s, Î³) -> rollout(mdp, s, Î³; max_steps = max_steps)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6612f482-2f10-43fa-9b7b-2f0c6a94b8e8„§cell_idÙ$6612f482-2f10-43fa-9b7b-2f0c6a94b8e8¤codeÙXrunepisode(mdp::MDP_TD; kwargs...) = runepisode(mdp, make_random_policy(mdp); kwargs...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$f424edac-388d-4465-900f-9459d2a88f79„§cell_idÙ$f424edac-388d-4465-900f-9459d2a88f79¤codeÚZ#take a step in the environment from state s using policy Ï€ and generate the subsequent action selection as well function init_step(mdp::MDP_TD{S, A, F, G, H}, Ï€::Matrix{T}, s::S) where {S, A, F<:Function, G<:Function, H<:Function, T<:Real} i_s = mdp.statelookup[s] i_a = sample_action(Ï€, i_s) a = mdp.actions[i_a] return (i_s, i_a, a) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$08e81e30-119d-4f4e-a865-f1f85cbffd31„§cell_idÙ$08e81e30-119d-4f4e-a865-f1f85cbffd31¤codeÚ‚md""" For many tasks the state space is too large to be solved effectively without prioritization. That being said, there are multiple methods of prioritizing states such as looking forward from states that are being visited frequently by the policy. Prioritized sweeping also makes use of expected updates to update the priority queue (sweeping through model state action pairs) which could be very costly in environments with large numbers of actions. Also in stochastic environments, our model may not even be accurate for some low probability transitions which would be a waste to consider. An alternative is to use sample updates which are higher variance individually, but may win out in terms of rate of convergence per computation in environments with high stochasticity and large branching. In the next sections, we more closely examine the value of expected and sample updates. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$898f1b06-a34f-496b-99db-9ca23498cbee„§cell_idÙ$898f1b06-a34f-496b-99db-9ca23498cbee¤codeÚ”function prioritized_sweeping_deterministic(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, max_steps = 10000, n = 100, qinit = zero(T), Ïµinit = one(T)/10, Qinit = initialize_state_action_value(mdp; qinit=qinit), Ï€init = create_Ïµ_greedy_policy(Qinit, Ïµinit), decay_Ïµ = false, history_state::S = first(mdp.states), save_history = false, update_policy! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), history = zeros(Int64, length(mdp.actions), length(mdp.states)), #each column contains the index of the state reached from the state represented by the column index while taking the action represented by the row index. the values are initialized at 0 which represents a transition that has not occured state_transition_map = zeros(Int64, length(mdp.actions), length(mdp.states)), #each column contains the reward received from the state represented by the column index while taking the action represented by the row index. the state_transition_map can be used to determine if any of these values have been updated from the zero initialization reward_transition_map = zeros(T, length(mdp.actions), length(mdp.states)), Î¸ = one(T)/100, init_step = 0, isoptimal = l -> false) where {S, A, F, G, H, T<:AbstractFloat} Ï€ = copy(Ï€init) Q = copy(Qinit) Ïµ = Ïµinit terminds = findall(mdp.isterm(s) for s in mdp.states) Q[:, terminds] .= zero(T) num_updates = 0 vhold = zeros(T, length(mdp.actions)) #keep track of rewards per step and steps per episode rewards = zeros(T, max_steps) steps = zeros(Int64, num_episodes) #maintains a queue of state action pairs ranked by their priority with the largest value first priority_queue = PriorityQueue{Tuple{Int64, Int64}, T}(Base.Order.Reverse) function updateQ!(Q, i_s, i_a, i_sâ€², r) qmax = calculate_qmax(Q, i_sâ€²) Q[i_a, i_s] += Î±*(r + Î³*qmax - Q[i_a, i_s]) num_updates += 1 end calculate_priority(r, qmax, i_s, i_a) = abs(r + Î³*qmax - Q[i_a, i_s]) calculate_qmax(Q, i_s) = maximum(Q[i, i_s] for i in eachindex(mdp.actions)) function q_planning!(Q, queue) step = 1 while (step <= n) && !isempty(queue) (i_a, i_s) = first(keys(queue)) delete!(queue, (i_a, i_s)) i_sâ€² = state_transition_map[i_a, i_s] r = reward_transition_map[i_a, i_s] updateQ!(Q, i_s, i_a, i_sâ€², r) #loop for all state action pairs that the model predicts will lead to s qmax = calculate_qmax(Q, i_s) for i_sÌ„ in eachindex(mdp.states) for i_aÌ„ in eachindex(mdp.actions) if state_transition_map[i_aÌ„, i_sÌ„] == i_s rÌ„ = reward_transition_map[i_aÌ„, i_sÌ„] p = calculate_priority(rÌ„, qmax, i_sÌ„, i_aÌ„) if p > Î¸ priority_queue[(i_aÌ„, i_sÌ„)] = p end end end end step += 1 end end step = 1 ep = 1 optimal_path = false while (ep <= num_episodes) && (step <= max_steps) && !optimal_path s = mdp.state_init() l = 0 while !mdp.isterm(s) && step <= max_steps #take policy action based on Q (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€, s) #replace model value with observed transition state_transition_map[i_a, i_s] = i_sâ€² reward_transition_map[i_a, i_s] = r #calculate expected update from previous Q value p = calculate_priority(r, calculate_qmax(Q, i_sâ€²), i_s, i_a) if p > Î¸ priority_queue[(i_a, i_s)] = p end rewards[step] = r q_planning!(Q, priority_queue) step += 1 s = sâ€² #update policy for next state action selection vhold .= Q[:, i_sâ€²] update_policy!(vhold, Ïµ, sâ€²) Ï€[:, i_sâ€²] .= vhold l += 1 end steps[ep] = l optimal_path = isoptimal(l) ep += 1 end return Q, steps, rewards, state_transition_map, reward_transition_map, num_updates end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$08ff749b-f4ff-4639-99f5-48262aa4643e„§cell_idÙ$08ff749b-f4ff-4639-99f5-48262aa4643e¤codeÚ)md""" In the Dyna agents simulated transitions are started in the state-actino pairs selected uniformly at random from all previously experienced pairs, but a uniform selection is usually not the best. Planning can be much more efficient if updates are focused on particular state-action pairs. In particular if an environment has goal states such as the maze, then one-step TD updates will only be relevant along the trajectory leading to that goal. Not all environments have goal states, but the idea still applies to cases where only certain states are expected to be updated given a certain amount of data from environment interactions. We can therefore work *backward* from states we know will be updated to consider all of the relevant predecessor states. Using *prioritized sweeping*, we maintain a queue of every state-action pair whose estimated value would change nontrivially if updated, prioritized by the size of the change. When the top pair in the queue is updated, the effect on each of its predecessor pairs is computed. If the effect is greater than some small threshold, then the pair is inserted int the queue with a new priority. In this way the effects of changes are efficiently backpropagated backward until quienscence. A full implementation of priorized sweeping can be found below. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$245d9616-e0d2-497e-bfdd-4729a7215bfd„§cell_idÙ$245d9616-e0d2-497e-bfdd-4729a7215bfd¤codeÚ‡md""" The maze task is a horrible candidate for MCTS because it is solved so effectively with the other techniques that build accurate action value estimates for the entire maze. It is very likely that states will be revisited in this task as well and that information is useful to solve the problem. MCTS would be most useful for problems where we cannot even know the full state space and states are never seen again in the course of a single episode as well as for tasks whose episodes don't last very long. Blackjack might be a good example of this since the number of steps is usually one or two. Another example could be tic-tac-toe. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$e8a6e672-b860-404f-83c1-62a080f23112„§cell_idÙ$e8a6e672-b860-404f-83c1-62a080f23112¤code¸md""" ### Figure 8.8 """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$39ae7727-ea72-48f3-8d63-e03a9f607f87„§cell_idÙ$39ae7727-ea72-48f3-8d63-e03a9f607f87¤codeÚÎfunction make_dyna_maze(scale) scale_value(v) = ceil(Int64, v*scale) xmax = scale_value(9) ymax = scale_value(6) sterm = GridworldState(xmax, ymax) wall1 = Set(GridworldState(scale_value(3), y) for y in scale_value(3):scale_value(5)) wall2 = Set([GridworldState(scale_value(6), scale_value(3) - 1)]) wall3 = Set(GridworldState(xmax - 1, y) for y in scale_value(4):ymax) walls = union(wall1, wall2, wall3) iswall(s) = in(s, walls) start = GridworldState(1, scale_value(4)) optimal_length = xmax - 1 + (ymax - scale_value(3) + 1) + start.y - scale_value(3) + 1 (maze = make_gridworld(;start = start, xmax=xmax, ymax=ymax, sterm = sterm, iswall = iswall), iswall = iswall, optimal_length = optimal_length) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$fad6e3b9-6d6e-4ea0-ac3d-5e57374c7056„§cell_idÙ$fad6e3b9-6d6e-4ea0-ac3d-5e57374c7056¤codeÙð#if no policy is provided then the rollout will use a uniformly random policy rollout(mdp::MDP{S, A, F, G, H}, s::S, Î³::T; max_steps = Inf) where {S, A, F, G, H, T<:Real} = rollout(mdp, s, s -> rand(mdp.actions), Î³; max_steps = max_steps)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$d0b18699-7d3a-418d-9d15-be41f1643f09„§cell_idÙ$d0b18699-7d3a-418d-9d15-be41f1643f09¤codeÚfunction trajectory_bellman_optimal_value!(Q::Matrix{T}, Ï€::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, Î³::T, Ïµ::T, s0::S, isterm::Function) where {T <: Real, S, A} delt = zero(T) num_updates = 0 s = s0 i_s = mdp.state_index[s] make_Ïµ_greedy_policy!(Ï€, mdp, Q, Ïµ, i_s) while !isterm(s) (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€, s) delt = max(delt, bellman_optimal_update!(Q, i_s, i_a, mdp, Î³)) make_Ïµ_greedy_policy!(Ï€, mdp, Q, Ïµ, i_sâ€²) num_updates += 1 s = sâ€² end return delt, num_updates end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$cd1980ff-2f35-4599-b08c-2037ddf5e995„§cell_idÙ$cd1980ff-2f35-4599-b08c-2037ddf5e995¤codeÚ¹function figure_8_4â€²â€²(; max_steps1 = 1000, max_steps2 = 2000, ntrials = 10, n = 10, Î± = 0.1f0, Ïµ = 0.1f0, Îº = 0.01f0) x = 1:(max_steps1 + max_steps2) function test_algo(f) y = zeros(max_steps1 + max_steps2) for _ in 1:ntrials (Q, steps, rewards, history, stm, rtm) = f(blocking_maze1, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps1, n = n) (Q2, steps2, rewards2, _, _) = f(blocking_maze2, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps2, n = n, Qinit = Q, history = history, state_transition_map = stm, reward_transition_map = rtm, init_step = max_steps1) y .+= cumsum([rewards; rewards2]) end return x, y end (x1, y1) = test_algo((args...; kwargs...) -> tabular_dynaQplus(args...; Îº = Îº, kwargs...)) (x2, y2) = test_algo((args...; kwargs...) -> tabular_dynaQplusâ€²(args...; Îº = Îº, kwargs...)) plot([scatter(x = x, y = y1 ./ ntrials, name = "Dyna-Q+"), scatter(x = x, y = y2 ./ ntrials, name = "Dyna-Q+â€²")]) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$c32d0943-ba8f-438f-83b6-5ed42221f630„§cell_idÙ$c32d0943-ba8f-438f-83b6-5ed42221f630¤codeÚºfunction figure_8_5â€²â€²(; max_steps1 = 3000, max_steps2 = 3000, ntrials = 10, n = 50, Î± = 0.5f0, Ïµ = 0.2f0, Îº = 0.001f0) x = 1:(max_steps1 + max_steps2) function test_algo(f) y = zeros(max_steps1 + max_steps2) for _ in 1:ntrials (Q, steps, rewards, history, stm, rtm) = f(blocking_maze2, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps1, n = n) (Q2, steps2, rewards2, _, _) = f(blocking_maze3, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps2, n = n, Qinit = Q, history = history, state_transition_map = stm, reward_transition_map = rtm, init_step = max_steps1) y .+= cumsum([rewards; rewards2]) end return x, y end (x1, y1) = test_algo((args...; kwargs...) -> tabular_dynaQplus(args...; Îº = Îº, kwargs...)) (x2, y2) = test_algo((args...; kwargs...) -> tabular_dynaQplusâ€²(args...; Îº = Îº, kwargs...)) plot([scatter(x = x, y = y1 ./ ntrials, name = "Dyna-Q+"), scatter(x = x, y = y2 ./ ntrials, name = "Dyna-Q+â€²")]) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$fc137613-7b4b-414c-93af-eeb2ace5d67f„§cell_idÙ$fc137613-7b4b-414c-93af-eeb2ace5d67f¤codeÚÓfunction make_Ïµ_greedy_policy!(Ï€::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, Q::Matrix{T}, Ïµ::T, i_s::Int64) where {T<:Real,S,A} n = length(mdp.actions) maxq = -Inf for i_a in eachindex(mdp.actions) Ï€[i_a, i_s] = Q[i_a, i_s] maxq = max(maxq, Q[i_a, i_s]) end Ï€[:, i_s] .= (Ï€[:, i_s] .â‰ˆ maxq) Ï€[:, i_s] ./= (sum(Ï€[:, i_s]) / (one(T) - Ïµ)) Ï€[:, i_s] .+= Ïµ / n if isnan(Ï€[1, i_s]) @info "Q = $(Q[:, i_s]), maxq = $maxq" end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$cffc9f11-77d7-4076-aa3b-821f1c741f58„§cell_idÙ$cffc9f11-77d7-4076-aa3b-821f1c741f58¤codeÙ—uct(tree_values::Dict{S, Tuple{T, Dict{Int64, Tuple{T, T}}}}, s::S, i_a::Int64, ntot::T) where {S, T<:Real} = sqrt(log(ntot)/tree_values[s][2][i_a][1])¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$4b424a47-dfeb-4380-8ab8-8bd24a080c2e„§cell_idÙ$4b424a47-dfeb-4380-8ab8-8bd24a080c2e¤codeÙábegin newmaze = make_dyna_maze(2) newmaze2 = make_dyna_maze(3) [plot_path(dyna_maze; iswall = s -> in(s, maze_walls)) plot_path(newmaze.maze; iswall = newmaze.iswall) plot_path(newmaze2.maze; iswall = newmaze2.iswall)] end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$0f37ec0a-b737-478b-bf6a-027899250c4e„§cell_idÙ$0f37ec0a-b737-478b-bf6a-027899250c4e¤codeÚ¡#take a step in the environment from state s using policy Ï€ and generate the subsequent action selection as well function sarsa_step(mdp::MDP_TD{S, A, F, G, H}, Ï€::Matrix{T}, s::S, a::A) where {S, A, F<:Function, G<:Function, H<:Function, T<:Real} (r, sâ€²) = mdp.step(s, a) i_sâ€² = mdp.statelookup[sâ€²] i_aâ€² = sample_action(Ï€, i_sâ€²) aâ€² = mdp.actions[i_aâ€²] return (sâ€², i_sâ€², r, aâ€², i_aâ€²) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$8dbc76fd-ac73-47ca-983e-0e90023390e3„§cell_idÙ$8dbc76fd-ac73-47ca-983e-0e90023390e3¤code«figure8_2()¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$e4f73889-af82-4304-89d5-ee50172eb3da„§cell_idÙ$e4f73889-af82-4304-89d5-ee50172eb3da¤codeÚFfunction make_greedy_policy!(Ï€::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, Q::Matrix{T}) where {T<:Real,S,A} for i_s in eachindex(mdp.states) maxq = -Inf for i_a in eachindex(mdp.actions) maxq = max(maxq, Q[i_a, i_s]) end Ï€[:, i_s] .= (Q[:, i_s] .â‰ˆ maxq) Ï€[:, i_s] ./= sum(Ï€[:, i_s]) end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$636d768c-670d-4485-a1dd-2bab6cf086d0„§cell_idÙ$636d768c-670d-4485-a1dd-2bab6cf086d0¤codeÚ£function make_random_mdp(num_states, b) states = collect(1:num_states) actions = [1, 2] next_states = [rand(states, b) for _ in 1:num_states*2] rterm = randn() ptf = Dict((i_a, i_s) => Dict(zip([num_states+1; next_states[i_s*i_a]], zip([0.1; fill((1-0.1)/b, b)], [rterm; randn(b)]))) for i_s in eachindex(states) for i_a in eachindex(actions)) #all transitions from terminal state lead to terminal state ptf[(1, num_states + 1)] = Dict([num_states+1 => (1., 0.0)]) ptf[(2, num_states + 1)] = Dict([num_states+1 => (1., 0.0)]) # function step(s, a) # r = randn() # rand() <= 0.1 && return (r, 0) # #each action result in one of b next states all equally likely with a different random selection of b states for each state-action pair # sâ€² = rand(next_states[s*a]) # return (r, sâ€²) # end # MDP_TD([states; 0], actions, () -> 1, step, s -> s == 0) FiniteStochasticMDP([states; 0], actions, ptf) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$cd139745-1877-43a2-97a0-3333e544cbd8„§cell_idÙ$cd139745-1877-43a2-97a0-3333e544cbd8¤codeÚfunction figure8_2(;num_episodes = 50, Î± = 0.1f0, nlist = [0, 5, 50], Î³ = 0.95f0, samples = 5) traces = [begin step_avg = zeros(Int64, num_episodes) for s in 1:samples Random.seed!(s) (Q, steps, rewards, _, _) = tabular_dynaQ(dyna_maze, Î±, Î³; num_episodes = num_episodes, n = n) step_avg .+= steps end scatter(x = 2:num_episodes, y = step_avg[2:end] ./ samples, name = "$n planning steps") end for n in nlist] plot(traces, Layout(xaxis_title = "Episodes", yaxis_title = "Steps per episode")) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$ffdd925e-b2b4-4cb1-9d6f-b8c9397729f6„§cell_idÙ$ffdd925e-b2b4-4cb1-9d6f-b8c9397729f6¤codeÚ¸function q_policy_evaluation!(Q::Matrix{T}, Ï€::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, Î³::T; max_updates = typemax(Int64), Î¸ = eps(zero(T))) where {T <: Real, S, A} delt, num_updates = uniform_bellman_policy_value!(Q, Ï€, mdp, Î³) total_updates = num_updates while (delt > Î¸) && (total_updates <= max_updates) delt, num_updates = uniform_bellman_policy_value!(Q, Ï€, mdp, Î³) total_updates += num_updates end return Q end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$f369a092-420d-4660-b802-93f05d5e7972„§cell_idÙ$f369a092-420d-4660-b802-93f05d5e7972¤codeÚ function apply_uct!(v_hold::Vector{T}, tree_values::Dict{S, Tuple{T, Dict{Int64, Tuple{T, T}}}}, s::S, c::T) where {S, T<:Real} #for normal UCB selection, unvisited states have an infinite bonus v_hold .= T(Inf) d = tree_values[s][2] isempty(d) && return v_hold ntot = sum(t[1] for t in values(d)) @inbounds @fastmath for i in keys(d) #note that the only bonus values computed here are for actions that have been visited v_hold[i] = (d[i][2] / d[i][1]) + c * uct(tree_values, s, i, ntot) end return v_hold end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$1d97325a-8b9a-438d-a5f9-e17638e64627„§cell_idÙ$1d97325a-8b9a-438d-a5f9-e17638e64627¤codeÙsinitialize_state_value(mdp::MDP_TD; vinit::T = 0.0f0) where T<:AbstractFloat = ones(T, length(mdp.states)) .* vinit¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$113d2bc2-1f77-479f-86e5-a65b20672d7a„§cell_idÙ$113d2bc2-1f77-479f-86e5-a65b20672d7a¤codeÚ function double_expected_sarsa(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, qinit = zero(T), Ïµinit = one(T)/10, Qinit::Matrix{T} = initialize_state_action_value(mdp; qinit=qinit), decay_Ïµ = false, target_policy_function! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), behavior_policy_function! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), Ï€init_target::Matrix{T} = create_Ïµ_greedy_policy(Qinit, Ïµinit), Ï€init_behavior::Matrix{T} = create_Ïµ_greedy_policy(Qinit, Ïµinit), save_state::S = first(mdp.states), save_history = false) where {S, A, F, G, H, T<:AbstractFloat} terminds = findall(mdp.isterm(s) for s in mdp.states) Q1 = copy(Qinit) Q2 = copy(Qinit) Q1[:, terminds] .= zero(T) Q2[:, terminds] .= zero(T) Ï€_target1 = copy(Ï€init_target) Ï€_target2 = copy(Ï€init_target) Ï€_behavior = copy(Ï€init_behavior) vhold1 = zeros(T, length(mdp.actions)) vhold2 = zeros(T, length(mdp.actions)) vhold3 = zeros(T, length(mdp.actions)) #keep track of rewards and steps per episode as a proxy for training speed rewards = zeros(T, num_episodes) steps = zeros(Int64, num_episodes) if save_history action_history = Vector{A}(undef, num_episodes) end for ep in 1:num_episodes Ïµ = decay_Ïµ ? Ïµinit/ep : Ïµinit s = mdp.state_init() rtot = zero(T) l = 0 while !mdp.isterm(s) (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€_behavior, s) if save_history && (s == save_state) action_history[ep] = a end # q_expected = sum(Ï€_target[i, i_sâ€²]*(Q1[i, i_sâ€²]*toggle + Q2[i, i_sâ€²]*(1-toggle)) for i in eachindex(mdp.actions)) toggle = rand() < 0.5 q_expected = if toggle sum(Ï€_target2[i, i_sâ€²]*Q1[i, i_sâ€²] for i in eachindex(mdp.actions)) else sum(Ï€_target1[i, i_sâ€²]*Q2[i, i_sâ€²] for i in eachindex(mdp.actions)) end if toggle Q2[i_a, i_s] += Î±*(r + Î³*q_expected - Q2[i_a, i_s]) else Q1[i_a, i_s] += Î±*(r + Î³*q_expected - Q1[i_a, i_s]) end #update terms for next step if toggle vhold2 .= Q2[:, i_s] target_policy_function!(vhold2, Ïµ, s) Ï€_target2[:, i_s] .= vhold2 else vhold1 .= Q1[:, i_s] target_policy_function!(vhold1, Ïµ, s) Ï€_target1[:, i_s] .= vhold1 end vhold3 .= vhold1 .+ vhold2 behavior_policy_function!(vhold3, Ïµ, s) Ï€_behavior[:, i_s] .= vhold3 s = sâ€² l+=1 rtot += r end steps[ep] = l rewards[ep] = rtot end Q1 .+= Q2 Q1 ./= 2 plain_return = Q1, create_greedy_policy(Q1), steps, rewards save_history && return (plain_return..., action_history) return plain_return end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$27d12c1c-ddb0-4bc1-af51-3388ff806705„§cell_idÙ$27d12c1c-ddb0-4bc1-af51-3388ff806705¤codeÚÅmd""" #### Figure 8.3: Policies found by planning and nonplanning Dyna-Q agents after episode $(@bind num_episodes_8_3 NumberField(1:100, default = 2)). The arrows indicate the greedy action with respect to the learned state-action value function. Without planning, the only states with a policy update are those within n episodes of the goal where n is the number of episodes experienced so far in training. For the planning agent after 1 episode, at least one of the values will be updated so during future planning steps, that information can propagate through the other states through bootstrapping since we are free to sample transitions from states into the ones that already have a value update. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$f0e88db8-e3ee-4b74-923e-c34038024824„§cell_idÙ$f0e88db8-e3ee-4b74-923e-c34038024824¤code¯figure_8_5â€²()¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$b0df4dad-74c7-4469-a13f-5ef6bb81199f„§cell_idÙ$b0df4dad-74c7-4469-a13f-5ef6bb81199f¤codeÙªmd""" #### Figure 8.5: Average performance of Dyna-Q on shortcut task. The constant slope after learning the optimal policy indicates the shortcut is not exploited. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$cfdaa9c2-265f-4540-9d04-d1b7a72aee3e„§cell_idÙ$cfdaa9c2-265f-4540-9d04-d1b7a72aee3e¤code¬figure_8_7()¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$250ea9da-dea3-4bf3-932d-cdda6756ae33„§cell_idÙ$250ea9da-dea3-4bf3-932d-cdda6756ae33¤codeÚ±function runepisode(mdp::CompleteMDP{T, S, A}, s0::S, isterm::Function, Ï€::Matrix{T}; max_steps = Inf) where {T<:Real, S, A} s = s0 states = Vector{S}() actions = Vector{A}() push!(states, s) (_, _, r, sâ€², a, _) = takestep(mdp, Ï€, s) push!(actions, a) rewards = [r] step = 2 sterm = s if isterm(sâ€²) sterm = sâ€² else sterm = s end s = sâ€² #note that the terminal state will not be added to the state list while !isterm(s) && (step <= max_steps) push!(states, s) (_, _, r, sâ€², a, _) = takestep(mdp, Ï€, s) push!(actions, a) push!(rewards, r) s = sâ€² step += 1 if isterm(sâ€²) sterm = sâ€² end end return states, actions, rewards, sterm end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$bc295bb5-addb-4bcf-a3e3-c839ccc346bd„§cell_idÙ$bc295bb5-addb-4bcf-a3e3-c839ccc346bd¤codeºmd""" ### Maze Example """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$729197ce-2c27-467d-ba5f-47a1ecd539f2„§cell_idÙ$729197ce-2c27-467d-ba5f-47a1ecd539f2¤codeÚ`begin abstract type GridworldAction end struct Up <: GridworldAction end struct Down <: GridworldAction end struct Left <: GridworldAction end struct Right <: GridworldAction end struct UpRight <: GridworldAction end struct DownRight <: GridworldAction end struct UpLeft <: GridworldAction end struct DownLeft <: GridworldAction end struct Stay <: GridworldAction end struct GridworldState x::Int64 y::Int64 end rook_actions = [Up(), Down(), Left(), Right()] move(::Up, x, y) = (x, y+1) move(::Down, x, y) = (x, y-1) move(::Left, x, y) = (x-1, y) move(::Right, x, y) = (x+1, y) move(::UpRight, x, y) = (x+1, y+1) move(::UpLeft, x, y) = (x-1, y+1) move(::DownRight, x, y) = (x+1, y-1) move(::DownLeft, x, y) = (x-1, y-1) move(::Stay, x, y) = (x, y) apply_wind(w, x, y) = (x, y+w) const wind_vals = [0, 0, 0, 1, 1, 1, 2, 2, 1, 0] end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$1aa76f3d-6041-4886-a6cd-787bdf1ec63c„§cell_idÙ$1aa76f3d-6041-4886-a6cd-787bdf1ec63c¤code¾figure_8_5â€²â€²(;Ïµ = 0.01f0)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$3bf9e526-826d-42b8-84ee-75f1c7f79c69„§cell_idÙ$3bf9e526-826d-42b8-84ee-75f1c7f79c69¤codeÚVfunction display_rook_policy(v::Vector{T}; scale = 1.0) where T<:AbstractFloat @htl("""

""") end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$308fd488-a009-4de0-8b27-c1f6b0677fed„§cell_idÙ$308fd488-a009-4de0-8b27-c1f6b0677fed¤codeÙsuct(counts::Dict{S, Dict{Int64, T}}, s::S, i_a::Int64, ntot::T) where {S, T<:Real} = sqrt(log(ntot)/counts[s][i_a])¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$340ba72b-172a-4d92-99b2-17687ab511c7„§cell_idÙ$340ba72b-172a-4d92-99b2-17687ab511c7¤codeÚîmd""" > ### *Exercise 8.4 (programming)* > The exploration bonus described above actually changes the estimated values of states and actions. Is this necessary? Suppose the bonus $\kappa \sqrt{\tau}$ was used not in updates, but solely in action selection. That is, suppose the action selected was always that for which $Q(S_t,a) + \kappa \sqrt{\tau(S_t, a)}$ was maximal. Carry out a gridworld experiment that tests and illustrates the strengths and weaknesses of this alternate approach. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$44364e7f-1910-421a-b961-63fbbaac8230„§cell_idÙ$44364e7f-1910-421a-b961-63fbbaac8230¤codeÚƒfunction bellman_optimal_value!(V::Vector{T}, mdp::FiniteStochasticMDP{T, S, A}, Î³::T) where {T <: Real, S, A} delt = zero(T) @inbounds @fastmath @simd for i_s in eachindex(mdp.states) maxvalue = typemin(T) @inbounds @fastmath @simd for i_a in eachindex(mdp.actions) r_avg = zero(T) v_avg = zero(T) ptf = mdp.ptf[(i_a, i_s)] for i_sâ€² in keys(ptf) p = ptf[i_sâ€²][1] r = ptf[i_sâ€²][2] v_avg += p*V[i_sâ€²] r_avg += p*r end x = r_avg + Î³*v_avg maxvalue = max(maxvalue, x) end delt = max(delt, abs(maxvalue - V[i_s]) / (eps(abs(V[i_s])) + abs(V[i_s]))) V[i_s] = maxvalue end return delt end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$4f4551fe-54a9-4186-ab8f-3535dc2bf4c5„§cell_idÙ$4f4551fe-54a9-4186-ab8f-3535dc2bf4c5¤codeÚ md""" ## 8.3 When the Model Is Wrong In the maze example the model started out empty and then was only filled in with correct information. In general, we cannot expect to be so fortunate. Models may be incorrect because the environment is stochastic and only a limited number of samples have been observed, or simply because the environment has changed. When the model is incorrect, the planning process will likely compute a suboptimal policy. In some cases, the suboptimal poicy computed by planning quickly leads to the discovery and correction of the modeling error. This tends to happen when the model is optimistic in the sense of predicting greater reward or better state transitions than are actually possible. The planned policy attempts to exploit these opportunities and in doing so it discovers that they do not exist. ### Example 8.2: Blocking Maze Consider a maze with a barrier that separates the start from the goal except for an opening on one end. After a set number of time steps this path is blocked and replaced with a longer path open at the other end. Below is an example of the environment with a random trajectory before and after the blocking. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$f6486854-4892-4fb6-a805-de56b19b3571„§cell_idÙ$f6486854-4892-4fb6-a805-de56b19b3571¤codeÚ~function make_uct_plus(update_prior!::Function) function f(v_hold::Vector{T}, q_ests::Dict{S, Dict{Int64, T}}, counts::Dict{S, Dict{Int64, T}}, s::S, c::T) where {S, T<:Real} update_prior!(v_hold, s) if !isempty(q_ests[s]) ntot = zero(T) qsum = zero(T) visited_weight = zero(T) @inbounds @fastmath for i in keys(q_ests[s]) ntot += counts[s][i] qsum += counts[s][i] * q_ests[s][i] visited_weight += v_hold[i] end #sample from unvisited weights proportional to the probability that one of those is the best option given the prior unvisited_weight = one(T) - visited_weight #sample from the unvisited actions weighted by their prior probability if rand() < unvisited_weight v_hold ./= unvisited_weight @inbounds @fastmath for i in keys(q_ests[s]) v_hold[i] = zero(T) end else #sample from visited actions weighted by UCT v_hold .= zero(T) @inbounds @fastmath for i in keys(q_ests[s]) #note that the only bonus values computed here are for actions that have been visited v_hold[i] = q_ests[s][i] + c*uct(counts, s, i, ntot) end end end return v_hold end end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$fb5601b0-06d4-43c4-81a6-23a4a8f29f00„§cell_idÙ$fb5601b0-06d4-43c4-81a6-23a4a8f29f00¤codeÙŸfunction make_random_policy(mdp::MDP_TD; init::T = 1.0f0) where T <: AbstractFloat ones(T, length(mdp.actions), length(mdp.states)) ./ length(mdp.actions) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$82e5719c-bbdb-4a18-b2f0-ad746b6acd41„§cell_idÙ$82e5719c-bbdb-4a18-b2f0-ad746b6acd41¤codeÚÂfunction make_puct(update_prior!::Function) function f(v_hold::Vector{T}, q_ests::Dict{S, Dict{Int64, T}}, counts::Dict{S, Dict{Int64, T}}, s::S, c::T) where {S, T<:Real} update_prior!(v_hold, s) if !isempty(q_ests[s]) ntot = zero(T) qsum = zero(T) @inbounds @fastmath for i in keys(q_ests[s]) ntot += counts[s][i] qsum += counts[s][i] * q_ests[s][i] end qavg = qsum / ntot v_hold .= qavg .+ ((c * sqrt(ntot)) .* v_hold) @inbounds @fastmath for i in keys(q_ests[s]) #note that the only bonus values computed here are for actions that have been visited v_hold[i] = q_ests[s][i] + ((v_hold[i] - qavg) / (one(T) + counts[s][i])) end end return v_hold end end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$c62cc32c-0d29-4ea2-8284-ac4c883df6db„§cell_idÙ$c62cc32c-0d29-4ea2-8284-ac4c883df6db¤codeÚÎstruct MDP_TD{S, A, F<:Function, G<:Function, H<:Function} <: MDP{S, A, F, G, H} states::Vector{S} statelookup::Dict{S, Int64} actions::Vector{A} actionlookup::Dict{A, Int64} state_init::G #function that produces an initial state for an episode step::F #function that produces reward and updated state given a state action pair isterm::H #function that returns true if the state input is terminal function MDP_TD(states::Vector{S}, actions::Vector{A}, state_init::G, step::F, isterm::H) where {S, A, F<:Function, G<:Function, H<:Function} statelookup = makelookup(states) actionlookup = makelookup(actions) new{S, A, F, G, H}(states, statelookup, actions, actionlookup, state_init, step, isterm) end end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$8586d633-7c50-49ba-9b74-b5bdad27c317„§cell_idÙ$8586d633-7c50-49ba-9b74-b5bdad27c317¤codeÙ#figure_8_8(1000; num_samples = 200)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$bb439641-30bd-495d-ba70-06b2e27efdbd„§cell_idÙ$bb439641-30bd-495d-ba70-06b2e27efdbd¤codeÚ\function make_gridworld(;actions = rook_actions, sterm = GridworldState(8, 4), start = GridworldState(1, 4), xmax = 10, ymax = 7, stepreward = 0.0f0, termreward = 1.0f0, iscliff = s -> false, iswall = s -> false, cliffreward = -100f0, goal2 = GridworldState(start.x, ymax), goal2reward = 0.0f0, usegoal2 = false) states = [GridworldState(x, y) for x in 1:xmax for y in 1:ymax] boundstate(x::Int64, y::Int64) = (clamp(x, 1, xmax), clamp(y, 1, ymax)) function step(s::GridworldState, a::GridworldAction) (x, y) = move(a, s.x, s.y) sâ€² = GridworldState(boundstate(x, y)...) iswall(sâ€²) && return s return sâ€² end function isterm(s::GridworldState) s == sterm && return true usegoal2 && (s == goal2) && return true return false end function tr(s::GridworldState, a::GridworldAction) isterm(s) && return (0f0, s) sâ€² = step(s, a) iscliff(sâ€²) && return (cliffreward, start) x = Float32(isterm(sâ€²)) usegoal2 && (sâ€² == goal2) && return (goal2reward, goal2) r = (1f0 - x)*stepreward + x*termreward (r, sâ€²) end MDP_TD(states, actions, () -> start, tr, isterm) end ¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$aab6ca56-57ca-421e-9b71-e3e96681c4c5„§cell_idÙ$aab6ca56-57ca-421e-9b71-e3e96681c4c5¤codeÙ$md""" ## 8.6 Trajectory Sampling """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$41bb1f78-b83a-4a45-ba5c-faa94e112f45„§cell_idÙ$41bb1f78-b83a-4a45-ba5c-faa94e112f45¤codeÚ monte_carlo_tree_search(dyna_maze, 0.95f0, rollout(;max_steps = 10), s; nsims = 100, depth = 100, c = 1f0)[1]) |> first |> length for _ in 1:10) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$0fff8e1b-d0c2-49b8-93b4-8d1615c26690„§cell_idÙ$0fff8e1b-d0c2-49b8-93b4-8d1615c26690¤codeÙªbegin using Statistics, PlutoPlotly, Random, StatsBase, PlutoUI, HypertextLiteral, DataStructures, StaticArrays, Transducers, Serialization, Arrow TableOfContents() end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$4e1c115a-4020-4a5a-a79a-56056892a953„§cell_idÙ$4e1c115a-4020-4a5a-a79a-56056892a953¤codeÙ"md""" ## Gridworld Environment """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$324181f0-b890-4198-9b4b-c36547e6629a„§cell_idÙ$324181f0-b890-4198-9b4b-c36547e6629a¤codeÚ)function create_Ïµ_greedy_policy(Q::Matrix{T}, Ïµ::T; Ï€ = copy(Q), get_valid_inds = j -> 1:size(Q, 1)) where T<:Real vhold = zeros(T, size(Q, 1)) for j in 1:size(Q, 2) vhold .= Q[:, j] make_Ïµ_greedy_policy!(vhold, Ïµ; valid_inds = get_valid_inds(j)) Ï€[:, j] .= vhold end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$eded8b72-70f4-4579-ba69-2eca409fa684„§cell_idÙ$eded8b72-70f4-4579-ba69-2eca409fa684¤codeÚýfunction plot_path(mdp, states::Vector, sterm; title = "Optimal policy
path example", iscliff = s -> false, iswall = s -> false) xmax = maximum([s.x for s in mdp.states]) ymax = maximum([s.y for s in mdp.states]) start = mdp.state_init() goal = mdp.states[findlast(mdp.isterm(s) for s in mdp.states)] start_trace = scatter(x = [start.x + 0.5], y = [start.y + 0.5], mode = "text", text = ["S"], textposition = "left", showlegend=false) finish_trace = scatter(x = [goal.x + .5], y = [goal.y + .5], mode = "text", text = ["G"], textposition = "left", showlegend=false) path_traces = [scatter(x = [states[i].x + 0.5, states[i+1].x + 0.5], y = [states[i].y + 0.5, states[i+1].y + 0.5], line_color = "blue", mode = "lines", showlegend=false, name = "Optimal Path") for i in 1:length(states)-1] finalpath = scatter(x = [states[end].x + 0.5, sterm.x + .5], y = [states[end].y + 0.5, sterm.y + 0.5], line_color = "blue", mode = "lines", showlegend=false, name = "Optimal Path") h1 = 30*ymax traces = [start_trace; finish_trace; path_traces; finalpath] cliff_squares = filter(iscliff, mdp.states) for s in cliff_squares push!(traces, scatter(x = [s.x + 0.6], y = [s.y+0.5], mode = "text", text = ["C"], textposition = "left", showlegend = false)) end wall_squares = filter(iswall, mdp.states) for s in wall_squares push!(traces, scatter(x = [s.x + 0.8], y = [s.y+0.5], mode = "text", text = ["W"], textposition = "left", showlegend = false)) end plot(traces, Layout(xaxis = attr(showgrid = true, showline = true, gridwith = 1, gridcolor = "black", zeroline = true, linecolor = "black", mirror=true, tickvals = 1:xmax, ticktext = fill("", 10), range = [1, xmax+1]), yaxis = attr(linecolor="black", mirror = true, gridcolor = "black", showgrid = true, gridwidth = 1, showline = true, tickvals = 1:ymax, ticktext = fill("", ymax), range = [1, ymax+1]), width = max(30*xmax, 200), height = max(h1, 200), autosize = false, padding=0, paper_bgcolor = "rgba(0, 0, 0, 0)", title = attr(text = title, font_size = 14, x = 0.5))) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$563b6dbd-ce51-4904-b1cc-d766bd1fd1d6„§cell_idÙ$563b6dbd-ce51-4904-b1cc-d766bd1fd1d6¤codeÚ2@htl("""

$(plot_path(dyna_maze; title = "Random policy path example in Dyna Maze", max_steps = 10000, iswall = s -> in(s, maze_walls)))

$rook_action_display

""")¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6a4116c9-87cf-4ee7-8030-aa1150853984„§cell_idÙ$6a4116c9-87cf-4ee7-8030-aa1150853984¤code´show_mcts_solution()¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$ee0f55c6-e9c6-4199-9f27-5706f3c84863„§cell_idÙ$ee0f55c6-e9c6-4199-9f27-5706f3c84863¤codeÙ!md""" ### MCTS Implementation """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$caca8d95-e40e-4592-b29e-a7e8b19faeb5„§cell_idÙ$caca8d95-e40e-4592-b29e-a7e8b19faeb5¤codeÚ €md""" Modifying Dyna-Q+ with this method of action selection no longer uses the Ïµ-greedy policy for exploration as before and instead directly encourages exploration in the policy itself which no longer follows the value estimates directly. Now, during the planning steps, we still update the value function using existing data. The bonus reward for old experiences will only have an impact on the data collection by encouraging visits to states that have not been seen for a while. The original method instead operates under the assumption that the value estimate updates during planning may be underestimating the value of states visited a long time ago and will boost their value increasing the chance of action selection indirectly. One problem with the method that modifies the action selection directly is that once the agent has found an optimal policy, along the trajectory of that path many state action pairs will be frequently visited, so an agent may deviate from that path for one action which has not been attempted in a while, but then by the same logic, the transition immediately back to the optimal state will be favored over all others since that transition hasn't been visited for a long time as well. The agent may deviate slightly from the optimal path but never for long. With DynaQ+, however, eventually, unvisited states no matter how far away they are from the optimal path will eventually be visited since during planning those value bonuses will propagate throughout the whole environment. That may be one reason why in the shortcut maze task, the modified DynaQ+ agent shown above never finds the shortcut and falls into the same trap as unmodified DynaQ. If the goal is to find cases where the model is wrong even if it is far away in state space from the previously optimal policy, then DynaQ+ is a much more effective method than the one acting directly on action selection. Similar to the original DynaQ, if the model is correct, then this algorithm will follow the optimal policy more often than DynaQ+ because very few deviations from optimal are tollerated. You can see in both figures, once the environment is stable, the modified algorithm starts accumulating reward faster. For the bloacking maze task, this method might perform the best over a certain time horizon by finding the new path faster than DynaQ but also consistently sticking new to the optimum more than DyanQ+. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$24efe9b4-9308-4ad1-8ef0-69f6f93407c0„§cell_idÙ$24efe9b4-9308-4ad1-8ef0-69f6f93407c0¤codeÚÇmd""" > ### *Exercise 8.2* > Why did the Dyna agent with exploration bonus, Dyna-Q+, perform better in the first phase as well as in the second phase of the blocking and shortcut experiments? For the second phase, the maze changed in both cases so the exploration reward bonus in Dyna-Q+ encourages the policy to attempt different actions that have not been visited recently which would result in model updates that reflect the new environment. For the first phase where the model is accurate, this task may benefit from larger initial exploration than the Ïµ of 0.1 provides. In that case the Dyna-Q+ reward simply acts like if we had a larger Ïµ in the first place which may result in faster learning. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$ecd8742c-2e10-4814-b477-7024e85b7fa6„§cell_idÙ$ecd8742c-2e10-4814-b477-7024e85b7fa6¤codeÚ™function make_Ïµ_greedy_policy!(Ï€::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, Q::Matrix{T}, Ïµ::T) where {T<:Real,S,A} n = length(mdp.actions) for i_s in eachindex(mdp.states) maxq = -Inf for i_a in eachindex(mdp.actions) maxq = max(maxq, Q[i_a, i_s]) end Ï€[:, i_s] .= (Q[:, i_s] .â‰ˆ maxq) Ï€[:, i_s] ./= (sum(Ï€[:, i_s]) / (one(T) - Ïµ)) Ï€[:, i_s] .+= Ïµ / actions end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$beae0491-ed11-4edf-a136-d384578b088b„§cell_idÙ$beae0491-ed11-4edf-a136-d384578b088b¤codeÙƒmonte_carlo_tree_search(dyna_maze, 0.95f0, rollout(;max_steps = 10_000), dyna_maze.state_init(); nsims = 100, depth = 10, c = 1f0)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$562e824c-34b5-415d-b186-d8e2cf1980e7„§cell_idÙ$562e824c-34b5-415d-b186-d8e2cf1980e7¤codeÚ8function figure_8_5(; max_steps = 3000, ntrials = 10, n = 10, Î± = 0.1f0, Ïµ = 0.1f0) x = 1:2*max_steps y = zeros(2*max_steps) for _ in 1:ntrials (Q, steps, rewards, history, stm, rtm) = tabular_dynaQ(blocking_maze2, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps, n = n) (Q2, steps2, rewards2, _, _) = tabular_dynaQ(blocking_maze3, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps, n = n, Qinit = Q, history = history, state_transition_map = stm, reward_transition_map = rtm) y .+= cumsum([rewards; rewards2]) end plot(scatter(x = x, y = y ./ ntrials)) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$f5e52b2f-ea14-423d-8ca9-2ed68cd27c69„§cell_idÙ$f5e52b2f-ea14-423d-8ca9-2ed68cd27c69¤codeÚ°function show_grid_policy(mdp, Ï€, name; display_function = display_rook_policy, action_display = rook_action_display, scale = 1.0) width = maximum(s.x for s in mdp.states) height = maximum(s.y for s in mdp.states) start = mdp.state_init() termind = findfirst(mdp.isterm, mdp.states) sterm = mdp.states[termind] ngrid = width*height @htl("""

$(HTML(mapreduce(i -> """
$(display_function(Ï€[:, i], scale =0.8))
""", *, eachindex(mdp.states))))

$(action_display)

""") end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$eb9ba23d-bee5-4bb1-b3e1-fe40d9f681dc„§cell_idÙ$eb9ba23d-bee5-4bb1-b3e1-fe40d9f681dc¤codeÚûfunction check_policy(Ï€::Matrix{T}, mdp::MDP_TD) where {T <: AbstractFloat} #checks to make sure that a policy is defined over the same space as an MDP (n, m) = size(Ï€) num_actions = length(mdp.actions) num_states = length(mdp.states) @assert n == num_actions "The policy distribution length $n does not match the number of actions in the mdp of $(num_actions)" @assert m == num_states "The policy is defined over $m states which does not match the mdp state count of $num_states" return nothing end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$1eb9a2ad-4584-4d32-8abb-e0e0bc0a771b„§cell_idÙ$1eb9a2ad-4584-4d32-8abb-e0e0bc0a771b¤codeÙä#if no value estimation function is provided, use a random rollout monte_carlo_tree_search(mdp::MDP{S, A, F, G, H}, Î³::T, s::S; kwargs...) where {S, A, F, G, H, T<:Real} = monte_carlo_tree_search(mdp, Î³, rollout, s; kwargs...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6cf35193-dba5-4f78-a4ac-245dda7a0846„§cell_idÙ$6cf35193-dba5-4f78-a4ac-245dda7a0846¤codeÙºbegin_value_iteration_v(mdp::FiniteMDP{T,S,A}, Î³::T; Vinit::T = zero(T), kwargs...) where {T<:Real,S,A} = begin_value_iteration_v(mdp, Î³, Vinit .* ones(T, size(mdp.ptf, 1)); kwargs...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$39c96fc8-8259-46e3-88a0-a14eb6752b5c„§cell_idÙ$39c96fc8-8259-46e3-88a0-a14eb6752b5c¤codeÚ²function show_grid_value(mdp, Q, name; scale = 1.0, title = "", sigdigits = 2) width = maximum(s.x for s in mdp.states) height = maximum(s.y for s in mdp.states) start = mdp.state_init() termind = findfirst(mdp.isterm, mdp.states) sterm = mdp.states[termind] ngrid = width*height displayvalue(Q::Matrix, i) = round(maximum(Q[:, i]), sigdigits = sigdigits) displayvalue(V::Vector, i) = round(V[i], sigdigits = sigdigits) @htl("""

$title
$(HTML(mapreduce(i -> """
$(displayvalue(Q, i))
""", *, eachindex(mdp.states))))

""") end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$4494cb61-ee2c-467b-9bf6-0afb59023e91„§cell_idÙ$4494cb61-ee2c-467b-9bf6-0afb59023e91¤codeÚõfunction sarsa(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, qinit = zero(T), Ïµinit = one(T)/10, Qinit = initialize_state_action_value(mdp; qinit=qinit), Ï€init = create_Ïµ_greedy_policy(Qinit, Ïµinit), history_state::S = first(mdp.states), update_policy! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), save_history = false, decay_Ïµ = false) where {S, A, F, G, H, T<:AbstractFloat} terminds = findall(mdp.isterm(s) for s in mdp.states) Q = copy(Qinit) Q[:, terminds] .= zero(T) Ï€ = copy(Ï€init) vhold = zeros(T, length(mdp.actions)) #keep track of rewards and steps per episode as a proxy for training speed rewards = zeros(T, num_episodes) steps = zeros(Int64, num_episodes) if save_history action_history = Vector{A}(undef, num_episodes) end for ep in 1:num_episodes Ïµ = decay_Ïµ ? Ïµinit/ep : Ïµinit s = mdp.state_init() (i_s, i_a, a) = init_step(mdp, Ï€, s) rtot = zero(T) l = 0 while !mdp.isterm(s) (sâ€², i_sâ€², r, aâ€², i_aâ€²) = sarsa_step(mdp, Ï€, s, a) if save_history && (s == history_state) action_history[ep] = a end Q[i_a, i_s] += Î± * (r + Î³*Q[i_aâ€², i_sâ€²] - Q[i_a, i_s]) #update terms for next step vhold .= Q[:, i_s] update_policy!(vhold, Ïµ, s) Ï€[:, i_s] .= vhold s = sâ€² a = aâ€² i_s = i_sâ€² i_a = i_aâ€² l+=1 rtot += r end steps[ep] = l rewards[ep] = rtot end default_return = Q, Ï€, steps, rewards save_history && return (default_return..., action_history) return default_return end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$618b5f35-2df5-4ffb-a34f-add542691080„§cell_idÙ$618b5f35-2df5-4ffb-a34f-add542691080¤codeÚ^#take a step in the environment from state s using policy Ï€ represented by a distribution over state-action pairs function takestep(mdp, Ï€::Matrix{T}, s) where T<:Real i_s = mdp.statelookup[s] i_a = sample_action(Ï€, i_s) a = mdp.actions[i_a] (r, sâ€²) = mdp.step(s, a) i_sâ€² = mdp.statelookup[sâ€²] return (i_s, i_sâ€², r, sâ€², a, i_a) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$0f9080af-f166-4a78-a003-8df07f6c27d4„§cell_idÙ$0f9080af-f166-4a78-a003-8df07f6c27d4¤codeÚpHTML(""" """)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$f8bf29fe-568f-437f-ba82-6b861988a18e„§cell_idÙ$f8bf29fe-568f-437f-ba82-6b861988a18e¤codeÚÍfunction make_greedy_policy!(Ï€::Matrix{T}, mdp::FiniteDeterministicMDP{T, S, A}, V::Vector{T}, Î³::T) where {T<:Real,S,A} for i_s in eachindex(mdp.states) maxv = -Inf for i_a in eachindex(mdp.actions) i_sâ€² = mdp.state_transition_map[i_a, i_s] r = mdp.reward_transition_map[i_a, i_s] x = r + Î³*V[i_sâ€²] maxv = max(maxv, x) Ï€[i_a, i_s] = x end Ï€[:, i_s] .= (Ï€[:, i_s] .â‰ˆ maxv) Ï€[:, i_s] ./= sum(Ï€[:, i_s]) end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$0d0bbf62-b1ac-45f6-8a92-1e77b0709cb3„§cell_idÙ$0d0bbf62-b1ac-45f6-8a92-1e77b0709cb3¤codeÚ,function figure8_3(num_episodes; Î± = 0.1f0, nlist = [0, 50], Î³ = 0.95f0,) (Q1, _) = tabular_dynaQ(dyna_maze, Î±, Î³; num_episodes = num_episodes, n = first(nlist)) d1 = show_grid_policy(dyna_maze, create_greedy_policy(Q1), "test") (Q2, _) = tabular_dynaQ(dyna_maze, Î±, Î³; num_episodes = num_episodes, n = last(nlist)) d2 = show_grid_policy(dyna_maze, create_greedy_policy(Q2), "test") @htl("""

Without planning (n = $(first(nlist)))$d1

With planning (n = $(last(nlist)))$d2

""") end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$b15f1262-1acf-40e5-87a7-bc4b1b437a42„§cell_idÙ$b15f1262-1acf-40e5-87a7-bc4b1b437a42¤codeÚ¥function runepisode(mdp::MDP{S, A, F, G, H}, Ï€; max_steps = Inf) where {S, A, F, G, H} s = mdp.state_init() states = Vector{S}() actions = Vector{A}() push!(states, s) (_, _, r, sâ€², a, _) = takestep(mdp, Ï€, s) push!(actions, a) rewards = [r] step = 2 sterm = s if mdp.isterm(sâ€²) sterm = sâ€² else sterm = s end s = sâ€² #note that the terminal state will not be added to the state list while !mdp.isterm(s) && (step <= max_steps) push!(states, s) (_, _, r, sâ€², a, _) = takestep(mdp, Ï€, s) push!(actions, a) push!(rewards, r) s = sâ€² step += 1 if mdp.isterm(sâ€²) sterm = sâ€² end end return states, actions, rewards, sterm end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$9be963b9-f3a1-4f92-8ff9-f5be75ed52f2„§cell_idÙ$9be963b9-f3a1-4f92-8ff9-f5be75ed52f2¤codeÚÚfunction expected_sarsa(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, qinit = zero(T), Ïµinit = one(T)/10, Qinit = initialize_state_action_value(mdp; qinit=qinit), Ï€init = create_Ïµ_greedy_policy(Qinit, Ïµinit), update_policy! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), decay_Ïµ = false, save_history = false, save_state = first(mdp.states)) where {S, A, F, G, H, T<:AbstractFloat} terminds = findall(mdp.isterm(s) for s in mdp.states) Q = copy(Qinit) Q[:, terminds] .= zero(T) Ï€ = copy(Ï€init) vhold = zeros(T, length(mdp.actions)) #keep track of rewards and steps per episode as a proxy for training speed rewards = zeros(T, num_episodes) steps = zeros(Int64, num_episodes) if save_history action_history = Vector{A}(undef, num_episodes) end for ep in 1:num_episodes Ïµ = decay_Ïµ ? Ïµinit/ep : Ïµinit s = mdp.state_init() rtot = zero(T) l = 0 while !mdp.isterm(s) (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€, s) if save_history && (s == save_state) action_history[ep] = a end q_expected = sum(Ï€[i, i_sâ€²]*Q[i, i_sâ€²] for i in eachindex(mdp.actions)) Q[i_a, i_s] += Î±*(r + Î³*q_expected - Q[i_a, i_s]) #update terms for next step vhold .= Q[:, i_s] update_policy!(vhold, Ïµ, s) Ï€[:, i_s] .= vhold s = sâ€² l+=1 rtot += r end steps[ep] = l rewards[ep] = rtot end base_return = (Q, Ï€, steps, rewards) save_history && return (base_return..., action_history) return base_return end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$b3a5adcb-5343-44e9-9466-1c51c1143a0d„§cell_idÙ$b3a5adcb-5343-44e9-9466-1c51c1143a0d¤codeexample_8_4()¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$80affd41-b5e6-4b9c-b827-4e3b39bd7767„§cell_idÙ$80affd41-b5e6-4b9c-b827-4e3b39bd7767¤codeÚ|function value_iteration_v!(V, Î¸, mdp, Î³, nmax, valuelist) nmax <= 0 && return valuelist #update value function delt = bellman_optimal_value!(V, mdp, Î³) #add copy of value function to results list push!(valuelist, copy(V)) #halt when value function is no longer changing delt <= Î¸ && return valuelist value_iteration_v!(V, Î¸, mdp, Î³, nmax - 1, valuelist) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$9a1b250f-b404-4db3-a4b7-4cd33b79d921„§cell_idÙ$9a1b250f-b404-4db3-a4b7-4cd33b79d921¤codeÙåfunction create_greedy_policy(Q::Matrix{T}; c = 1000, Ï€ = copy(Q)) where T<:Real vhold = zeros(T, size(Q, 1)) for j in 1:size(Q, 2) vhold .= Q[:, j] make_greedy_policy!(vhold; c = c) Ï€[:, j] .= vhold end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$db66615e-fbbc-4ea8-b529-bdc14e58a215„§cell_idÙ$db66615e-fbbc-4ea8-b529-bdc14e58a215¤codeÚ€begin const block3 = Set(GridworldState(x, 3) for x in 2:8) const blocking_maze3 = make_gridworld(;iswall = s -> in(s, block3), block_maze_base_args...) [plot_path(blocking_maze2, iswall = s -> in(s, block2), max_steps = 2000, title = "Shortcut Maze Steps <= 3000") plot_path(blocking_maze3, iswall = s -> in(s, block3), max_steps = 2000, title = "Shortcut Maze Steps > 3000")] end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$72b40384-9ca1-4bc1-8e1a-8b639d39e215„§cell_idÙ$72b40384-9ca1-4bc1-8e1a-8b639d39e215¤codeÚmd""" In this section we compare two ways of distributing updates. The classical approach, from dynamic programming, is to perform sweeps through the entire state (or state-action) space, updating each state (or state-action pair) once per sweep. This is problematic on large tasks because there may not be time to complete even one sweep. In many tasks, the vast majority or the states are irrelevant because they are visited only under very poor policies or with very low probability. Exhaustive sweeps implicitely devote equal time to all parts of the state space rather than focusing where it is needed. As we discussed in Chapter 4, exhaustive sweeps and the equal treatment of all states that they imply are not necessary properties of dynamic programming. In principle, updates can be distributed in any way one likes (to assure convergence, all states or state-action pairs must be visited in the limit an infinite number of times; although an exception to this is discussed in Section 8.7 below), but in practice exhaustive sweeps are often used. The second approach is to sample from the state or state-action space according to some distribution. One could sample uniformly, as in the Dyna-Q agent, but this would suffer from some of the same problems as exhaustive sweeps. More appealing is to distribute updates according to the on-poliy distribution, that is according to the distribution observed when following the current policy. One advantage of this distribution is that it is easily generated; one simply interacts with the model, following the current policy. In an episodic task, one starts in a start state (or according to the starting-state distribution) and simulates until the terminal state. In a continuing task, one starts anywhere and just keeps simulating. In either case, sample state transitions and rewards are given by the model, and sample actions are given by the current policy. In other words, one simulates explicit individual trajectories and performs updates at the state or state-action pairs encountered along the way. We call this way of generating experience and updates *trajectory sampling*. Is the on-policy distribution of updates a good one? Intuitively it seems like a good choice, at least better than the uniform distribution. For example, if you are learning to play chess, you study positions that might arise in real games, not random positions of chess pieces. The latter may be valid states, but to be able to accurately value them is a different skill from evaluating positions in real games. We will also see in Part II that the on policy distribution has significant advantages when function approximation is used. Whether or not function approximation is used, one might expect on-policy focusing to significantly improve the speed of planning. The following experiment attempts to assess empirically the value of sampling from the on policy distribution by comparing it directly to the alternative of *uniform* sampling. In the *uniform* case, all state action pairs are updated once in place and in the *on-policy* case an episode is imulated starting from the same state and updating each state-action pair that occured under the current $\epsilon$-greedy policy $(\epsilon = 0.1)$. The tasks were undiscounted episodic tasks, generated randomly as follows. From each of the $\vert \mathcal{S} \vert$ states, two actions were possible, each of which results in one of $b$ next states, all equally likely, with a different random selection of $b$ states for each state-action pair. The branching factor, $b$, was the same for all state-action pairs. In addition, on all transitions there was a 0.1 probability of transition to the terminal state, ending the episode. The expected reward on each transition was selected from a Gaussian distribution with mean 0 and variance 1. At any point in the planning process one can stop and exhaustively compute $v_{\tilde \pi}(s_0)$, the true value of the start state under the greedy policy, $\tilde \pi$, given the current action-value function $Q$, as in indication of how well the agent would do on a new episode on which it acted greedily (all while assuming the model is correct). """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$63bf9d16-4516-4cec-895f-f010275bca16„§cell_idÙ$63bf9d16-4516-4cec-895f-f010275bca16¤codeÙ+md""" ## 8.5 Expected vs Sample Updates """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$0adcbce8-2be5-48ef-af43-04815e10dc5c„§cell_idÙ$0adcbce8-2be5-48ef-af43-04815e10dc5c¤codeÚhfunction make_greedy_policy!(v::AbstractVector{T}; c = 1000) where T<:Real (vmin, vmax) = extrema(v) isnan(vmin) && error("NaN values in vector") if isinf(vmax) v .= T.(isinf.(v)) v ./= sum(v) elseif vmin == vmax v .= zero(T) v .= one(T) / length(v) else v .= (v .- vmax) ./ abs(vmin - vmax) v .= exp.(c .* v) v ./= sum(v) end return v end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$f9e4baec-c988-4abd-9bb0-c618c0ec07b9„§cell_idÙ$f9e4baec-c988-4abd-9bb0-c618c0ec07b9¤codeÚ.#perform a rollout with an mdp from state s using a policy function Ï€ that produces an action selection given a state input. return value is an unbiased estimate of the value of this state under the policy function rollout(mdp::MDP{S, A, F, G, H}, s::S, Ï€::Function, Î³::T; max_steps = Inf) where {S, A, F, G, H, T<:Real} step = 0 #note that the terminal state will not be added to the state list r = zero(T) while !mdp.isterm(s) && (step <= max_steps) a = Ï€(s) r, sâ€² = mdp.step(s, a) r += Î³^step * r s = sâ€² step += 1 end return r end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$502a7125-4460-4d39-be14-4852fb6d9ad2„§cell_idÙ$502a7125-4460-4d39-be14-4852fb6d9ad2¤codeÙˆplot_path(mdp; title = "Random policy
path example", kwargs...) = plot_path(mdp, make_random_policy(mdp); title = title, kwargs...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$31072f9b-de1b-42cf-a187-cbff99b49b50„§cell_idÙ$31072f9b-de1b-42cf-a187-cbff99b49b50¤codeÚ–md""" Throughout the book we have considered a variety of value-function updates for the state value $v(s)$ and the state-action value $q(s, a)$. Broadly, they can be separated into expected updates and sample-updates. The first expected update considered was for dynamic programming in which we make use of the full probability transition function $p(s^\prime, r \vert s, a)$. When used with a policy $\pi$ that provides a probability distribution over actions for each state, this technique is called *policy evaluation* and when a maximization is used over actions to find optimal value function and policy policy, this corresponds to *value iteration*. Both of these techniques could be used with either the state or state-action value function. The expected update for a state-action pair, $s,a$, is: $Q(s, a) \leftarrow \sum_{s^\prime,r}\hat p (s^\prime,r \vert s, a) \left [ \gamma \max_{a^\prime} Q(s^\prime, a^\prime) \right ] \tag{8.1}$ Considering just one-step sample updates, we use an environment to sample a transition: $(s, a) \rightarrow (s^\prime, r)$. Since we do not use the full probability transition function, the updates make use of a learning rate and accumulate samples over time either with Sarsa or Q-learning. The corresponding sample update for $s,a$ given a sample next state and reward, $S^\prime$ and $R$ (from the model), is the Q-learning-like update: $Q(s, a) \leftarrow Q(s, a) + \alpha \left [ R + \gamma \max_{a^\prime} Q(s^\prime, a^\prime) - Q(s, a) \right ] \tag{8.2}$ where $\alpha$ is the usual positive step-size parameter. The difference between expected and sample updates is significant to the extent that the environment is stochastic, specifically, to the extent that, given a state and action, many possible next states may occur with various probabilities. If only one next state is possible, then the expected and sample updates given above are identical (taking $\alpha=1$). If there are many possible next states, then tehre may be significant differences. In favor of the expected update is that it is an exact computation, resulting in a new $Q(s, a)$ whose correctness is limited only by the correctness of the $Q(s^\prime, a^\prime)$ at successor states. The sample update is in addition affected by sampling error. On the other hand, the sample update is cheaper computationally because it considers only one next state, not all possible next states. In practice, the computation required by update operations is usually dominated by the number of state-action pairs at which $Q$ is evaluated. For a particular starting pair, $s, a$, let $b$ be the *branching factor* (i.e. the number or possible next states, $s^\prime$, for which $\hat p (s^\prime, s, a) \gt 0$). Then an expected update of this pair requires roughly $b$ times as much computation as a sample update. If there is enough time to complete an expected update, then the resulting estimate is generally better than that of $b$ sample updates because of the absense of sampling error. But if there is insufficient time to complete an expected update, then sample updates are always preferable because they at least make some improvement int he value estimate with fewer than $b$ updates. In a large problem with many state-action pairs, we are often in the latter situation. With so many state-action pairs, expected updates of all of them would take a very long time. Before that we may be much better off with a few sample updates at many state-action pairs than with expected updates at a few pairs. Given a unit of computation effort, is it better devoted to a few expected updates or to $b$ times as many sample updates? Figure 8.7 shows the results of any analysis that suggests and answer ot this question. It shows the estimation error as a function of computation time for expected and sample updates for a variety of branching factors $b$. The case considered is that in which all $b$ successor states are equally likely and in which the error in the initial estimate is 1. The values at the next states are assumed correct, sot he expected update redues the error to zero upon its completion. In this case, sample updates reduce the error according to $\sqrt{\frac{b-1}{bt}}$ where $t$ is the number of sample updates that have been performed (assuming same averages, i.e. $\alpha = 1/t$). The key observation is that for moderately large $b$ the error falls dramatically with a tiny fraction of $b$ updates. For these cases, many state-action pairs could have their values improved dramatically, to within a few percent of the effect of an expected update, in teh same time that a single state-action pair could undergo an expected update. The advantage of sample updates shown in Figure 8.7 is probably an underestimate of the real effect. In a real problem, the values of successor states would be estimates that are themselves updated. By causing estimates to be more accurate sooner, sample updates will have a second advantage in that the values backed up from the successor states will be more accurate. These results suggest that sample updates are likely to be superior to expected updates on problems with large stochastic branching factors and too many states to be solved exactly. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$77fde69f-2119-41eb-8993-a93b2c47ca7e„§cell_idÙ$77fde69f-2119-41eb-8993-a93b2c47ca7e¤codeÙ$md""" ## Gridworld Visualization """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$65818e67-c146-4686-a9aa-d0859ef662fb„§cell_idÙ$65818e67-c146-4686-a9aa-d0859ef662fb¤codeÙ$md""" ### Example 8.1: Dyna Maze """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$b03087e9-e15d-4563-bdae-4d9ba7d2cec6„§cell_idÙ$b03087e9-e15d-4563-bdae-4d9ba7d2cec6¤codeÙ$md""" ## MDP Types and Functions """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$195d2a34-c44c-4088-8ec4-dece3107f16d„§cell_idÙ$195d2a34-c44c-4088-8ec4-dece3107f16d¤codeÚefunction bellman_optimal_value!(V::Vector{T}, mdp::FiniteMDP{T, S, A}, Î³::T) where {T <: Real, S, A} delt = zero(T) @inbounds @fastmath @simd for i_s in eachindex(mdp.states) maxvalue = typemin(T) @inbounds @fastmath @simd for i_a in eachindex(mdp.actions) x = zero(T) for (i_r, r) in enumerate(mdp.rewards) @inbounds @fastmath @simd for i_sâ€² in eachindex(V) x += mdp.ptf[i_sâ€², i_r, i_a, i_s] * (r + Î³ * V[i_sâ€²]) end end maxvalue = max(maxvalue, x) end delt = max(delt, abs(maxvalue - V[i_s]) / (eps(abs(V[i_s])) + abs(V[i_s]))) V[i_s] = maxvalue end return delt end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6858ef8b-1ca7-4e96-b57e-26553423cc13„§cell_idÙ$6858ef8b-1ca7-4e96-b57e-26553423cc13¤codeÙTsample_action(v::Vector{T}) where T<:AbstractFloat = sample(1:length(v), weights(v))¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$e689df6b-d6f0-4928-9212-a940aa00b0ef„§cell_idÙ$e689df6b-d6f0-4928-9212-a940aa00b0ef¤codeÙÏhtml""" """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$3b0d2c55-2123-4b51-b946-6bc352e3d00a„§cell_idÙ$3b0d2c55-2123-4b51-b946-6bc352e3d00a¤codeÚømd""" > ### *Exercise 8.6* > The analysis above assumed that all of the $b$ possible next states were equally likely to occur. Suppose instead that the distribution was highly skewed, that some of the $b$ states were much more likely to occur than most. Would this strengthen or weaken the case for sample updates over expected updates? Support your answer. For the expected updates, all transitions no matter how small the probability are given the same amount of computation time. If certain transitions are much more likely than others, than the resulting state value computation is dominated by those transitions. If we perform sample updates, those transitions will appear more frequently than the less likely ones and will contribute earlier to the error reduction. In the extreme case if just one transition is close to probability 1, then that will be sampled almost every time and after just a single sample the error could be reduced close to 0. An expected update, on the other hand, would have to consider every state action pair to just perform a single update. Therefore the efficiency of sample updates would become exaggerated in all real world problems where the subsequent state transition probabilities deviate from a uniform distribution. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$c04c91be-de42-4dfc-bd0d-b9fbdde0c9cf„§cell_idÙ$c04c91be-de42-4dfc-bd0d-b9fbdde0c9cf¤codeÚkfunction simulate!(visit_counts, Q, mdp, Î³::T, v_est, s, depth, c::T, v_hold, update_tree_policy!, updateQ!, updateV!, q_hold, apply_bonus!, step_kwargs, est_kwargs) where T<:Real #if the state is terminal, produce a value of 0 mdp.isterm(s) && return zero(T) depth â‰¤ 0 && return v_est(mdp, s, Î³; est_kwargs...) #for a state where no actions have been attempted, expand a new node if !haskey(visit_counts, s) # Q[s] = sparse(q_hold) # visit_counts[s] = sparse(q_hold) Q[s] = Dict{Int64, T}() visit_counts[s] = Dict{Int64, T}() return v_est(mdp, s, Î³; est_kwargs...) end apply_bonus!(v_hold, Q, visit_counts, s, c) update_tree_policy!(v_hold, s) i_a = sample_action(v_hold) a = mdp.actions[i_a] r, sâ€² = mdp.step(s, a; step_kwargs...) q = r + Î³*simulate!(visit_counts, Q, mdp, Î³, v_est, sâ€², depth - 1, c, v_hold, update_tree_policy!, updateQ!, updateV!, q_hold, apply_bonus!, step_kwargs, est_kwargs) updateV!(visit_counts, one(T), s, i_a) # Î´q = (q - Q[s][i_a]) / visit_counts[s][i_a] Î´q = (q - get_dict_value(Q[s], i_a)) / visit_counts[s][i_a] updateQ!(Q, Î´q, s, i_a) return q end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$d4e1e807-7e87-4b43-9c36-f59999dfcd2d„§cell_idÙ$d4e1e807-7e87-4b43-9c36-f59999dfcd2d¤codeÙÅ#get the value of a dictionary d at key k, defaulting to some value when the key is absent get_dict_value(d::Dict{K, V}, k::K; default = zero(V)) where {K, V<:Real} = haskey(d, k) ? d[k] : default ¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$e25ec0d5-f70f-4269-b2a1-efa194936f72„§cell_idÙ$e25ec0d5-f70f-4269-b2a1-efa194936f72¤codeÙ$abstract type MDP{S, A, F, G, H} end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$1f7d77a6-d774-436d-a745-5a160cc15f2b„§cell_idÙ$1f7d77a6-d774-436d-a745-5a160cc15f2b¤codeÙµfunction plot_path(mdp, Ï€; max_steps = 100, kwargs...) (states, actions, rewards, sterm) = runepisode(mdp, Ï€; max_steps = max_steps) plot_path(mdp, states, sterm; kwargs...) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$5b688057-06c7-4ae4-95d6-0a2ff451f11c„§cell_idÙ$5b688057-06c7-4ae4-95d6-0a2ff451f11c¤codeÚvfunction figure_8_4â€²(; max_steps1 = 1000, max_steps2 = 2000, ntrials = 10, n = 10, Î± = 0.1f0, Ïµ = 0.1f0, Îº = 0.01f0) x = 1:(max_steps1 + max_steps2) function test_algo(f) y = zeros(max_steps1 + max_steps2) for _ in 1:ntrials (Q, steps, rewards, history, stm, rtm) = f(blocking_maze1, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps1, n = n) (Q2, steps2, rewards2, _, _) = f(blocking_maze2, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps2, n = n, Qinit = Q, history = history, state_transition_map = stm, reward_transition_map = rtm, init_step = max_steps1) y .+= cumsum([rewards; rewards2]) end return x, y end (x1, y1) = test_algo(tabular_dynaQ) (x2, y2) = test_algo((args...; kwargs...) -> tabular_dynaQplus(args...; Îº = Îº, kwargs...)) plot([scatter(x = x, y = y1 ./ ntrials, name = "Dyna-Q"), scatter(x = x, y = y2 ./ ntrials, name = "Dyna-Q+")]) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$8987052b-0828-43a5-982e-5f3d6209f2aa„§cell_idÙ$8987052b-0828-43a5-982e-5f3d6209f2aa¤codeÙ‰@bind fig_8_4_params PlutoUI.combine() do Child md""" Planning Steps: $(Child(:n, NumberField(1:100, default = 5))) """ end |> confirm¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$4b2a4fb1-7395-4293-9ff9-e2f9da50f56b„§cell_idÙ$4b2a4fb1-7395-4293-9ff9-e2f9da50f56b¤codeÚfunction value_iteration_q!(Q, Î¸, mdp, Î³, nmax) nmax <= 0 && return Q #update value function delt = bellman_optimal_value!(Q, mdp, Î³) #halt when value function is no longer changing delt <= Î¸ && return Q value_iteration_q!(Q, Î¸, mdp, Î³, nmax - 1) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$fcaddc19-c0c9-4e8e-8f84-adc7a02cc1f5„§cell_idÙ$fcaddc19-c0c9-4e8e-8f84-adc7a02cc1f5¤codeÙTfigure_8_8(10_000; blist = [1, 3], num_samples = 10, expected_updates_max = 200_000)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$5afad18b-1d87-450e-a0ff-8c1249d663ed„§cell_idÙ$5afad18b-1d87-450e-a0ff-8c1249d663ed¤codeÚ function tabular_dynaQ(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, max_steps = 10000, n = 100, qinit = zero(T), Ïµinit = one(T)/10, Qinit = initialize_state_action_value(mdp; qinit=qinit), Ï€init = create_Ïµ_greedy_policy(Qinit, Ïµinit), decay_Ïµ = false, history_state::S = first(mdp.states), save_history = false, update_policy! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), history = Dict{Int64, Set{Int64}}(), #each column contains the index of the state reached from the state represented by the column index while taking the action represented by the row index. the values are initialized at 0 which represents a transition that has not occured state_transition_map = zeros(Int64, length(mdp.actions), length(mdp.states)), #each column contains the reward received from the state represented by the column index while taking the action represented by the row index. the state_transition_map can be used to determine if any of these values have been updated from the zero initialization reward_transition_map = zeros(T, length(mdp.actions), length(mdp.states)), init_step = 0, isoptimal = l -> false) where {S, A, F, G, H, T<:AbstractFloat} Ï€ = copy(Ï€init) Q = copy(Qinit) Ïµ = Ïµinit terminds = findall(mdp.isterm(s) for s in mdp.states) Q[:, terminds] .= zero(T) num_updates = 0 vhold = zeros(T, length(mdp.actions)) #keep track of rewards per step and steps per episode rewards = zeros(T, max_steps) steps = zeros(Int64, num_episodes) function updateQ!(Q, i_s, i_a, i_sâ€², r) qmax = maximum(Q[i, i_sâ€²] for i in eachindex(mdp.actions)) Q[i_a, i_s] += Î±*(r + Î³*qmax - Q[i_a, i_s]) num_updates += 1 end function q_planning!(Q, history) for _ in 1:n i_s = rand(keys(history)) i_a = rand(history[i_s]) i_sâ€² = state_transition_map[i_a, i_s] r = reward_transition_map[i_a, i_s] updateQ!(Q, i_s, i_a, i_sâ€², r) end end ep = 1 step = 1 optimal_path = false while (step <= max_steps) && (ep <= num_episodes) && !optimal_path s = mdp.state_init() l = 0 while !mdp.isterm(s) && step <= max_steps (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€, s) updateQ!(Q, i_s, i_a, i_sâ€², r) state_transition_map[i_a, i_s] = i_sâ€² reward_transition_map[i_a, i_s] = r #save state action pair visited if haskey(history, i_s) push!(history[i_s], i_a) else history[i_s] = Set([i_a]) end q_planning!(Q, history) Ï€ = create_Ïµ_greedy_policy(Q, Ïµ; Ï€ = Ï€) s = sâ€² rewards[step] = r step += 1 l += 1 end steps[ep] = l optimal_path = isoptimal(l) ep += 1 end return Q, steps[1:ep-1], rewards[1:step-1], history, state_transition_map, reward_transition_map, num_updates end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$b9054ed4-7f16-4920-b13e-5f4c6f50dcf3„§cell_idÙ$b9054ed4-7f16-4920-b13e-5f4c6f50dcf3¤codeÚ function tabular_dynaQplus(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, max_steps = 10000, n = 100, qinit = zero(T), Ïµinit = one(T)/10, Qinit = initialize_state_action_value(mdp; qinit=qinit), Ï€init = create_Ïµ_greedy_policy(Qinit, Ïµinit), decay_Ïµ = false, history_state::S = first(mdp.states), save_history = false, update_policy! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), history = zeros(Int64, length(mdp.actions), length(mdp.states)), #each column contains the index of the state reached from the state represented by the column index while taking the action represented by the row index. the values are initialized to produce transitions to the original state state_transition_map = mapreduce(i_s -> i_s .* ones(Int64, length(mdp.actions)), hcat, eachindex(mdp.states)), #each column contains the reward received from the state represented by the column index while taking the action represented by the row index. the state_transition_map can be used to determine if any of these values have been updated from the zero initialization reward_transition_map = zeros(T, length(mdp.actions), length(mdp.states)), Îº = 0.1f0, init_step = 0) where {S, A, F, G, H, T<:AbstractFloat} Ï€ = copy(Ï€init) Q = copy(Qinit) Ïµ = Ïµinit terminds = findall(mdp.isterm(s) for s in mdp.states) Q[:, terminds] .= zero(T) vhold = zeros(T, length(mdp.actions)) #keep track of rewards per step and steps per episode rewards = zeros(T, max_steps) steps = zeros(Int64, num_episodes) function updateQ!(Q, i_s, i_a, i_sâ€², r) qmax = maximum(Q[i, i_sâ€²] for i in eachindex(mdp.actions)) Q[i_a, i_s] += Î±*(r + Î³*qmax - Q[i_a, i_s]) end function q_planning!(Q, history, step) for _ in 1:n i_s = ceil(Int64, rand()*length(mdp.states)) i_a = ceil(Int64, rand()*length(mdp.actions)) i_sâ€² = state_transition_map[i_a, i_s] Ï„ = step - history[i_a, i_s] r = reward_transition_map[i_a, i_s] + Îº*sqrt(Ï„) updateQ!(Q, i_s, i_a, i_sâ€², r) end end step = 1 ep = 1 while (ep <= num_episodes) && (step <= max_steps) s = mdp.state_init() l = 0 while !mdp.isterm(s) && step <= max_steps (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€, s) updateQ!(Q, i_s, i_a, i_sâ€², r) state_transition_map[i_a, i_s] = i_sâ€² reward_transition_map[i_a, i_s] = r #save the step for which that state action pair was visited history[i_a, i_s] = step + init_step rewards[step] = r q_planning!(Q, history, step + init_step) step += 1 Ï€ = create_Ïµ_greedy_policy(Q, Ïµ; Ï€ = Ï€) s = sâ€² l += 1 end steps[ep] = l ep += 1 end return Q, steps, rewards, history, state_transition_map, reward_transition_map end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$5ae2d740-13c7-4568-8f04-25bc82fecbdb„§cell_idÙ$5ae2d740-13c7-4568-8f04-25bc82fecbdb¤codeÚ„begin const block1 = Set(GridworldState(x, 3) for x in 1:8) const block2 = Set(GridworldState(x, 3) for x in 2:9) const block_maze_base_args = (xmax = 9, ymax = 6, sterm = GridworldState(9, 6), start = GridworldState(4, 1)) const blocking_maze1 = make_gridworld(;iswall = s -> in(s, block1), block_maze_base_args...) const blocking_maze2 = make_gridworld(;iswall = s -> in(s, block2), block_maze_base_args...) [plot_path(blocking_maze1, iswall = s -> in(s, block1), max_steps = 2000, title = "Blocking Maze Steps <= 1000") plot_path(blocking_maze2, iswall = s -> in(s, block2), max_steps = 1000, title = "Blocking Maze Steps > 1000")] end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$0dbd2b87-d000-408b-8d04-25fc0fa512d1„§cell_idÙ$0dbd2b87-d000-408b-8d04-25fc0fa512d1¤codeÙKfigure_8_4â€²(;ntrials = 10, Ïµ = 0.2f0, Î± = 1.0f0, Îº = 0.0001f0, n = 50)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$33f66659-1a87-4890-9137-dbc7776a19d8„§cell_idÙ$33f66659-1a87-4890-9137-dbc7776a19d8¤codeÚÿfunction make_greedy_policy!(Ï€::Matrix{T}, mdp::FiniteMDP{T, S, A}, V::Vector{T}, Î³::T) where {T<:Real,S,A} for i_s in eachindex(mdp.states) maxv = -Inf for i_a in eachindex(mdp.actions) x = zero(T) for i_r in eachindex(mdp.rewards) for i_sâ€² in eachindex(V) x += mdp.ptf[i_sâ€², i_r, i_a, i_s] * (mdp.rewards[i_r] + Î³ * V[i_sâ€²]) end end maxv = max(maxv, x) Ï€[i_a, i_s] = x end Ï€[:, i_s] .= (Ï€[:, i_s] .â‰ˆ maxv) Ï€[:, i_s] ./= sum(Ï€[:, i_s]) end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$269f4505-e807-446c-8fd8-3458482e00ab„§cell_idÙ$269f4505-e807-446c-8fd8-3458482e00ab¤codeÚ function tabular_dynaQplusâ€²(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, max_steps = 10000, n = 100, qinit = zero(T), Ïµinit = one(T)/10, Qinit = initialize_state_action_value(mdp; qinit=qinit), Ï€init = create_Ïµ_greedy_policy(Qinit, Ïµinit), decay_Ïµ = false, history_state::S = first(mdp.states), save_history = false, update_policy! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ), history = zeros(Int64, length(mdp.actions), length(mdp.states)), #each column contains the index of the state reached from the state represented by the column index while taking the action represented by the row index. the values are initialized to produce transitions to the original state state_transition_map = mapreduce(i_s -> i_s .* ones(Int64, length(mdp.actions)), hcat, eachindex(mdp.states)), #each column contains the reward received from the state represented by the column index while taking the action represented by the row index. the state_transition_map can be used to determine if any of these values have been updated from the zero initialization reward_transition_map = zeros(T, length(mdp.actions), length(mdp.states)), Îº = 0.1f0, init_step = 0) where {S, A, F, G, H, T<:AbstractFloat} Ï€ = copy(Ï€init) Q = copy(Qinit) Ïµ = Ïµinit terminds = findall(mdp.isterm(s) for s in mdp.states) Q[:, terminds] .= zero(T) vhold = zeros(T, length(mdp.actions)) #keep track of rewards per step and steps per episode rewards = zeros(T, max_steps) steps = zeros(Int64, num_episodes) function updateQ!(Q, i_s, i_a, i_sâ€², r) qmax = maximum(Q[i, i_sâ€²] for i in eachindex(mdp.actions)) Q[i_a, i_s] += Î±*(r + Î³*qmax - Q[i_a, i_s]) end function q_planning!(Q, history) for _ in 1:n i_s = ceil(Int64, rand()*length(mdp.states)) i_a = ceil(Int64, rand()*length(mdp.actions)) i_sâ€² = state_transition_map[i_a, i_s] r = reward_transition_map[i_a, i_s] updateQ!(Q, i_s, i_a, i_sâ€², r) end end step = 1 ep = 1 while (ep <= num_episodes) && (step <= max_steps) s = mdp.state_init() l = 0 while !mdp.isterm(s) && step <= max_steps (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€, s) updateQ!(Q, i_s, i_a, i_sâ€², r) state_transition_map[i_a, i_s] = i_sâ€² reward_transition_map[i_a, i_s] = r #save the step for which that state action pair was visited history[i_a, i_s] = step + init_step rewards[step] = r q_planning!(Q, history) step += 1 Ï€ .= Q .+ Îº .* sqrt.(T.(step .+ init_step .- history)) Ï€ = create_greedy_policy(Ï€; Ï€ = Ï€) s = sâ€² l += 1 end steps[ep] = l ep += 1 end return Q, steps, rewards, history, state_transition_map, reward_transition_map end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$037f1804-b24e-46e7-b2a8-6747e669db66„§cell_idÙ$037f1804-b24e-46e7-b2a8-6747e669db66¤codeÙ?makelookup(v::Vector) = Dict(x => i for (i, x) in enumerate(v))¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$c1ff1bea-649c-4483-b4be-55134f0e8cb7„§cell_idÙ$c1ff1bea-649c-4483-b4be-55134f0e8cb7¤codeÙƒfunction sample_action(Ï€::Matrix{T}, i_s::Integer) where T<:AbstractFloat (n, m) = size(Ï€) sample(1:n, weights(Ï€[:, i_s])) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$7ae23e8e-d554-4d26-a08a-83dab507af13„§cell_idÙ$7ae23e8e-d554-4d26-a08a-83dab507af13¤codeÙinitialize_state_action_value(mdp::MDP_TD; qinit::T = 0.0f0) where T<:AbstractFloat = ones(T, length(mdp.actions), length(mdp.states)) .* qinit¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$26fe0c28-8f0f-4cff-87fb-76f04fce1be1„§cell_idÙ$26fe0c28-8f0f-4cff-87fb-76f04fce1be1¤codeÚ#md""" > ### *Exercise 8.3* > Careful inspection of Figure 8.5 reveals that the difference between Dyna-Q+ and Dyna-Q narrowed slightly over the first part of the experiment. What is the reason for this? As long as the environment isn't changing, no matter how small Ïµ is both algorithms will converge to the optimal policy as long as $\alpha$, $\kappa$, and $\tau$ converge to 0. For a given value of $\epsilon$, the Dyna-Q+ agent will take more exploratory actions due to the modified rewards updating the Q function. If both agents have converged to close to the optimal policy at this point, then the agent which follows a more expoitative policy will produce higher rewards. In this case, the extra exploration of Dyna-Q+ is not beneficial and its relative performance to Dyna-Q degrades. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$98547223-05a6-43da-80b2-63c67d2de283„§cell_idÙ$98547223-05a6-43da-80b2-63c67d2de283¤codeÙ!figure_8_5(;ntrials = 10, n = 10)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$4b3604db-0c1b-4770-95b1-5f5bb34d071b„§cell_idÙ$4b3604db-0c1b-4770-95b1-5f5bb34d071b¤codeÙ:function addelements(e1, e2) @htl(""" $e1 $e2 """) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$143fff7d-0bb2-43b4-b810-53784fe848bd„§cell_idÙ$143fff7d-0bb2-43b4-b810-53784fe848bd¤codeÚ¶function q_learning(mdp::MDP_TD{S, A, F, G, H}, Î±::T, Î³::T; num_episodes = 1000, qinit = zero(T), Ïµinit = one(T)/10, Qinit = initialize_state_action_value(mdp; qinit=qinit), Ï€init = create_Ïµ_greedy_policy(Qinit, Ïµinit), decay_Ïµ = false, history_state::S = first(mdp.states), save_history = false, update_policy! = (v, Ïµ, s) -> make_Ïµ_greedy_policy!(v, Ïµ)) where {S, A, F, G, H, T<:AbstractFloat} terminds = findall(mdp.isterm(s) for s in mdp.states) Q = copy(Qinit) Q[:, terminds] .= zero(T) Ï€ = copy(Ï€init) vhold = zeros(T, length(mdp.actions)) #keep track of rewards and steps per episode as a proxy for training speed rewards = zeros(T, num_episodes) steps = zeros(Int64, num_episodes) if save_history history_actions = Vector{A}(undef, num_episodes) end for ep in 1:num_episodes Ïµ = decay_Ïµ ? Ïµinit/ep : Ïµinit s = mdp.state_init() rtot = zero(T) l = 0 while !mdp.isterm(s) (i_s, i_sâ€², r, sâ€², a, i_a) = takestep(mdp, Ï€, s) if save_history && (s == history_state) history_actions[ep] = a end qmax = maximum(Q[i, i_sâ€²] for i in eachindex(mdp.actions)) Q[i_a, i_s] += Î±*(r + Î³*qmax - Q[i_a, i_s]) #update terms for next step vhold .= Q[:, i_s] update_policy!(vhold, Ïµ, s) Ï€[:, i_s] .= vhold s = sâ€² l+=1 rtot += r end steps[ep] = l rewards[ep] = rtot end save_history && return Q, Ï€, steps, rewards, history_actions return Q, Ï€, steps, rewards end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$6778296c-ab05-47e7-86d2-e98c075a8a0c„§cell_idÙ$6778296c-ab05-47e7-86d2-e98c075a8a0c¤codeÙËfunction begin_value_iteration_q(mdp::M, Î³::T, Q::Matrix{T}; Î¸ = eps(zero(T)), nmax=typemax(Int64)) where {T<:Real, M <: CompleteMDP{T, S, A} where {S, A}} value_iteration_q!(Q, Î¸, mdp, Î³, nmax) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$c04c803c-cdca-4b8b-9c9d-e456ee677906„§cell_idÙ$c04c803c-cdca-4b8b-9c9d-e456ee677906¤codeÙôbegin const maze_walls = Set(GridworldState(x, y) for (x, y) in [(3, 3), (3, 4), (3, 5), (6, 2), (8, 4), (8, 5), (8, 6)]) const dyna_maze = make_gridworld(;xmax = 9, ymax = 6, sterm = GridworldState(9, 6), iswall = s -> in(s, maze_walls)) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$9d69687a-8df6-4e74-aa99-fbfcc84bcccf„§cell_idÙ$9d69687a-8df6-4e74-aa99-fbfcc84bcccf¤codeÚqconst rook_action_display = @htl("""

Actions

""")¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$16c68a13-c295-4a64-bc2b-2ae8451f332f„§cell_idÙ$16c68a13-c295-4a64-bc2b-2ae8451f332f¤codeÚ#function bellman_optimal_value!(V::Vector{T}, mdp::FiniteDeterministicMDP{T, S, A}, Î³::T) where {T <: Real, S, A} delt = zero(T) @inbounds @fastmath @simd for i_s in eachindex(mdp.states) maxvalue = typemin(T) @inbounds @fastmath @simd for i_a in eachindex(mdp.actions) i_sâ€² = mdp.state_transition_map[i_a, i_s] r = mdp.reward_transition_map[i_a, i_s] x = r + Î³*V[i_sâ€²] maxvalue = max(maxvalue, x) end delt = max(delt, abs(maxvalue - V[i_s]) / (eps(abs(V[i_s])) + abs(V[i_s]))) V[i_s] = maxvalue end return delt end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$fb00aedd-e103-4463-b4f8-d0dce6275c64„§cell_idÙ$fb00aedd-e103-4463-b4f8-d0dce6275c64¤codeÚ§function example_8_4(;Î± = 1f0, n = 5, Î³ = 0.95f0, samples = 2, maze_scales = [1, 1.25, 1.5, 2]) function get_optimal_steps(algo, mdp, optimal_steps) updates_until_optimal = 0 for s in 1:samples Random.seed!(s) out = algo(mdp, Î±, Î³; n = n, Ïµinit = 0.1f0, isoptimal = l -> l == optimal_steps) updates_until_optimal += last(out) end return updates_until_optimal / samples end mazes = [make_dyna_maze(s) for s in maze_scales] function make_trace(algo, name) y = mazes |> Map(maze -> get_optimal_steps(algo, maze.maze, maze.optimal_length)) |> tcollect x = [length(maze.maze.states) for maze in mazes] scatter(x = x, y = y, name = name) end traces = [make_trace(prioritized_sweeping_deterministic, "prioritized sweeping"), make_trace(tabular_dynaQ, "Dyna-Q")] plot(traces, Layout(yaxis_type = "log", xaxis_type = "log", xaxis_title = "Maze States", yaxis_title = "Updates until optimal solution")) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$e43513e8-2517-43b7-9a16-e57d4125edc4„§cell_idÙ$e43513e8-2517-43b7-9a16-e57d4125edc4¤codeÙWrunepisode(mdp::MDP_MC; kwargs...) = runepisode(mdp, s -> rand(mdp.actions); kwargs...)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$df62fd47-6627-4931-b429-964c65960446„§cell_idÙ$df62fd47-6627-4931-b429-964c65960446¤codeÚtfunction figure_8_7(;bvec = [2, 10, 100, 1000, 10_000]) traces = [begin t = 1:2*b error_reduction = sqrt.((b-1) ./ (b.*t)) scatter(x = [0; t ./ b], y = [1; error_reduction], name = "b = $b") end for b in bvec] ex_update_trace = scatter(x = [0, 1, 1, 2], y = [1, 1, 0, 0], name = "expected updates", mode = "lines", line_color = "gray") md""" ### Figure 8.7: Comparison of efficiency of expected and sample updates $(plot([traces; ex_update_trace], Layout(yaxis_title = "RMS error in value estimates", xaxis=attr(title = "Number of max Q computations", tickvals = [0, 1, 2], ticktext = [0, "1b", "2b"])))) """ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$321bdf5a-bff7-4181-986f-d3884ea96d27„§cell_idÙ$321bdf5a-bff7-4181-986f-d3884ea96d27¤code¸md""" # Dependencies """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$82e1ceb8-b1bb-4dea-b041-bf462041793f„§cell_idÙ$82e1ceb8-b1bb-4dea-b041-bf462041793f¤codeÚ#function make_greedy_policy!(Ï€::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, V::Vector{T}, Î³::T) where {T<:Real,S,A} for i_s in eachindex(mdp.states) maxv = -Inf for i_a in eachindex(mdp.actions) r_avg = zero(T) v_avg = zero(T) ptf = mdp.ptf[(i_a, i_s)] for i_sâ€² in keys(ptf) p = ptf[i_sâ€²][1] v_avg += p*V[i_sâ€²] r_avg += p*ptf[i_sâ€²][2] end x = r_avg + Î³*v_avg maxv = max(maxv, x) Ï€[i_a, i_s] = x end Ï€[:, i_s] .= (Ï€[:, i_s] .â‰ˆ maxv) Ï€[:, i_s] ./= sum(Ï€[:, i_s]) end return Ï€ end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$5e49504e-9623-48f9-aeb5-360906b92a09„§cell_idÙ$5e49504e-9623-48f9-aeb5-360906b92a09¤codeÙGvholdtest = [[1f0, 1f0, 1f0]; fill(Inf32, length(nyt_valid_words) - 3)]¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$2ff6d187-e06f-47b5-9834-d06bfc820c26„§cell_idÙ$2ff6d187-e06f-47b5-9834-d06bfc820c26¤codeÚffunction figure_8_4(; max_steps1 = 1000, max_steps2 = 2000, ntrials = 10, n = 10, Î± = 0.1f0, Ïµ = 0.1f0) y = zeros(max_steps1 + max_steps2) x = 1:(max_steps1 + max_steps2) for _ in 1:ntrials (Q, steps, rewards, history, stm, rtm) = tabular_dynaQ(blocking_maze1, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps1, n = n) (Q2, steps2, rewards2, _, _) = tabular_dynaQ(blocking_maze2, Î±, 0.95f0; Ïµinit = Ïµ, max_steps = max_steps2, n = n, Qinit = Q, history = history, state_transition_map = stm, reward_transition_map = rtm) y .+= cumsum([rewards; rewards2]) end plot(scatter(x = x, y = y ./ ntrials)) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$c31f4646-aa8a-41e3-9c68-ae8a349d4ed1„§cell_idÙ$c31f4646-aa8a-41e3-9c68-ae8a349d4ed1¤codeÙtfigure_8_4â€²â€²(;ntrials = 10, Ïµ = 0.1f0, Î± = 1.0f0, Îº = 0.0001f0, n = 50, max_steps1 = 1000, max_steps2 = 2000)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$dab6eac0-7958-4d3a-a1af-781fb57e7adb„§cell_idÙ$dab6eac0-7958-4d3a-a1af-781fb57e7adb¤codeÚšmd""" ### Example 8.3: Shortcut Maze The problem of an incorrect model can be even worse in the case of an improvement to the environment. The following example illustrates the problem in which a maze is at first blocked forcing a longer path. After a certain number of timesteps a shorter path is opened up without disturbing the original path. The Dyna-Q agent however never learns to switch to the shortcut because the model never reveals it or provides incentive to explore paths that it already thinks are suboptimal. Even with an Ïµ-greedy policy, it is very unlikely that hte agent will take so many exploratory actions as to discover the shortcut. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$04ea981e-337e-4324-a5cc-178eb3c7605b„§cell_idÙ$04ea981e-337e-4324-a5cc-178eb3c7605b¤codeÙ)md""" ## 8.11 Monte Carlo Tree Search """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$976ff0c3-54aa-41f0-b963-baf77bea8cb8„§cell_idÙ$976ff0c3-54aa-41f0-b963-baf77bea8cb8¤codeÚ7md""" The general problem here is the conflict between exploration and exploitation. In a planning context, exploration means trying actions that improve the model, whereas exploitation means behaving in the optimal way given the current model. We want the agent to explore to find changes in the environment, but not so much that performance is greatly degraded. As in the earlier exploration/exploitation conflict, there probably is no solution that is both perfect and practical, but simple heuristics are often effective. An example of such a heuristic is that used by the Dyna-Q+ agent which keeps track of how many time steps have elapsed since the state-action pair was last tried in a real interaction with the environment. The more time that has elapsed, the greater (we might presume) the chance that the dynamics of this pair has changed and that the model is incorrect. To encourage behavior that tests long-untried actions, a special "bonus reward" is given on simulated experiences involving these actions. In particular, if the modeled reward for a transition is $r$, and the transition has not been tried in $\tau$ time steps, then planning updates are done as if that transition produced a reward of $r + \kappa \sqrt{\rho}$, for some small $\kappa$. This encourages the agent to keep testing all accessible state transitions and even to find long sequences of actions in order to carry out such tests. Of course all this testing has its cost, but in many cases, as in the shortcut maze, this kind of computational curiosity is well worth the extra exploration. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$094321bc-2d44-4e67-9ac6-5216a42e0cd3„§cell_idÙ$094321bc-2d44-4e67-9ac6-5216a42e0cd3¤codeÚŒfunction bellman_policy_update!(Q::Matrix{T}, Ï€::Matrix{T}, i_s::Int64, i_a::Int64, mdp::FiniteStochasticMDP{T, S, A}, Î³::T) where {T <: Real, S, A} #perform a bellman optimal update for a given state action pair index and return the percentage change in value q_avg = zero(T) r_avg = zero(T) ptf = mdp.ptf[(i_a, i_s)] x = zero(T) for i_sâ€² in keys(ptf) (p, r) = ptf[i_sâ€²] v = zero(T) @inbounds @fastmath @simd for i_aâ€² in eachindex(mdp.actions) v += Ï€[i_aâ€², i_sâ€²] * Q[i_aâ€², i_sâ€²] end x += p*(r + Î³ * v) end delt = abs(x - Q[i_a, i_s]) / (eps(abs(Q[i_a, i_s])) + abs(Q[i_a, i_s])) Q[i_a, i_s] = x return delt end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$13f08473-f0d8-47d1-aa48-de3e4a083dbf„§cell_idÙ$13f08473-f0d8-47d1-aa48-de3e4a083dbf¤codeÚxfunction simulate!(w::AS, tree_values::Dict{S, Tuple{T, Dict{Int64, Tuple{T, T}}}}, mdp::AfterstateMDP_MC{S, AS, A, F, G, H, I}, Î³::T, v_est::Function, depth::Integer, c::T, v_hold, update_tree_policy!, update_tree!, q_hold, apply_bonus!, step_kwargs, transition_kwargs, est_kwargs) where {T<:Real, S, AS, A, F<:Function, G<:Function, H<:Function, I<:Function} dist = mdp.afterstate_transition(w; transition_kwargs...) #get the distribution of states following the transition k_sample = sample(collect(keys(dist)), weights(collect(values(dist)))) #sample one of the transition states to visit in the tree sum(begin (r, s) = k p = dist[k] vâ€² = simulate!(s, k == k_sample, tree_values, mdp, Î³, v_est, depth - 1, c, v_hold, update_tree_policy!, update_tree!, q_hold, apply_bonus!, step_kwargs, transition_kwargs, est_kwargs) p * (r + Î³ * vâ€²) end for k in keys(dist)) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$f143446c-e44b-4d75-baa7-3b24eafad003„§cell_idÙ$f143446c-e44b-4d75-baa7-3b24eafad003¤codeÚ ^#need to decide which tree statistics to collect like state values or afterstate values and what expansion means vs normal mcts. I know that when I visit a new afterstate which is the same as a new action selection, I want to estimate it with a weighted sum of the value estimates of all the sucessor states but I don't necessarily want the tree search to continue down all those paths and split although it could so a single simulation would split into all the successor states avoiding the need to make a selection. For doing sample updates though, I want to just pick one of those branches to go down by sampling from the distribution so then the simulation function itself should handle the case of an unvisited state which would look at the afterstate values that lead from that state if any exist and well this is the problem is which values should be saved and what does it mean to estimate the value of something for one of the unvisited states function simulate!(s::S, visit::Bool, tree_values::Dict{S, Tuple{T, Dict{Int64, Tuple{T, T}}}}, mdp::AfterstateMDP_MC{S, AS, A, F, G, H, I}, Î³::T, v_est::Function, depth::Integer, c::T, v_hold, update_tree_policy!, update_tree!, q_hold, apply_bonus!, step_kwargs, transition_kwargs, est_kwargs) where {T<:Real, S, AS, A, F<:Function, G<:Function, H<:Function, I<:Function} #if the state is terminal, produce a value of 0 mdp.isterm(s) && return zero(T) depth â‰¤ 0 && return v_est(mdp, s, Î³; est_kwargs...) #for a state where no actions have been attempted, expand a new node if !haskey(tree_values, s) v = v_est(mdp, s, Î³; est_kwargs...) tree_values[s] = (v, Dict{Int64, Tuple{T, T}}()) return v end !visit && return max(tree_values[s][1], maximum(t[2]/t[1] for t in values(tree_values[s][2]); init = zero(T))) #if not visiting this state then just return the best value estimate and do not update the tree values #compute value estimates and bonus applies to each potential action apply_bonus!(v_hold, tree_values, s, c) update_tree_policy!(v_hold, s) #select an action from the tree policy i_a = sample_action(v_hold) a = mdp.actions[i_a] r1, w = mdp.afterstate_step(s, a; step_kwargs...) #take a step with the action and get the afterstate v_w = simulate!(w, tree_values, mdp, Î³, v_est, depth, c, v_hold, update_tree_policy!, update_tree!, q_hold, apply_bonus!, step_kwargs, transition_kwargs, est_kwargs) v_a = r1 + v_w #value for the visited action update_tree!(tree_values, v_a, s, i_a) return max(tree_values[s][1], maximum(t[2]/t[1] for t in values(tree_values[s][2]); init = zero(T))) #the value that was just updated will be included in this maximum end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$0899f37c-5def-4d15-8ca3-ebdec8e96b43„§cell_idÙ$0899f37c-5def-4d15-8ca3-ebdec8e96b43¤codeÚIfunction begin_value_iteration_v(mdp::M, Î³::T, V::Vector{T}; Î¸ = eps(zero(T)), nmax=typemax(Int64)) where {T<:Real, M <: CompleteMDP{T, S, A} where {S, A}} valuelist = [copy(V)] value_iteration_v!(V, Î¸, mdp, Î³, nmax, valuelist) Ï€ = form_random_policy(mdp) make_greedy_policy!(Ï€, mdp, V, Î³) return (valuelist, Ï€) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$94b339bb-6e2d-422f-8043-615e8be9a217„§cell_idÙ$94b339bb-6e2d-422f-8043-615e8be9a217¤codeÚ begin abstract type CompleteMDP{T<:Real, S, A} end struct FiniteMDP{T<:Real, S, A} <: CompleteMDP{T, S, A} states::Vector{S} actions::Vector{A} rewards::Vector{T} # ptf::Dict{Tuple{S, A}, Matrix{T}} ptf::Array{T, 4} action_scratch::Vector{T} state_scratch::Vector{T} reward_scratch::Vector{T} state_index::Dict{S, Int64} action_index::Dict{A, Int64} function FiniteMDP{T, S, A}(states::Vector{S}, actions::Vector{A}, rewards::Vector{T}, ptf::Array{T, 4}) where {T <: Real, S, A} new(states, actions, rewards, ptf, Vector{T}(undef, length(actions)), Vector{T}(undef, length(states)+1), Vector{T}(undef, length(rewards)), Dict(zip(states, eachindex(states))), Dict(zip(actions, eachindex(actions)))) end end FiniteMDP(states::Vector{S}, actions::Vector{A}, rewards::Vector{T}, ptf::Array{T, 4}) where {T <: Real, S, A} = FiniteMDP{T, S, A}(states, actions, rewards, ptf) struct FiniteDeterministicMDP{T<:Real, S, A} <: CompleteMDP{T, S, A} states::Vector{S} actions::Vector{A} state_index::Dict{S, Int64} action_index::Dict{A, Int64} state_transition_map::Matrix{Int64} #index of state reached from the state corresponding to the column when taking action corresponding to the row reward_transition_map::Matrix{T} #reward received for the transition from the state corresponding to the column when taking action corresponding to the row end FiniteDeterministicMDP(states::Vector{S}, actions::Vector{A}, state_transition_map::Matrix{Int64}, reward_transition_map::Matrix{T}) where {T<:Real, S, A} = FiniteDeterministicMDP{T, S, A}(states, actions, makelookup(states), makelookup(actions), state_transition_map, reward_transition_map) struct FiniteStochasticMDP{T<:Real, S, A} <: CompleteMDP{T, S, A} states::Vector{S} actions::Vector{A} state_index::Dict{S, Int64} action_index::Dict{A, Int64} ptf::Dict{Tuple{Int64, Int64}, Dict{Int64, Tuple{T, T}}} #for each state action pair index there is a corresponding dictionary mapping each transition state index to the probability of that transition and the average reward received end FiniteStochasticMDP(states::Vector{S}, actions::Vector{A}, ptf::Dict{Tuple{Int64, Int64}, Dict{Int64, Tuple{T, T}}}) where {T<:Real, S, A} = FiniteStochasticMDP{T, S, A}(states, actions, makelookup(states), makelookup(actions), ptf) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$466b1cbf-586f-4b53-8b4b-2dc32e1c8b0a„§cell_idÙ$466b1cbf-586f-4b53-8b4b-2dc32e1c8b0a¤codeÚ¹#perform action selection within an mdp for a given state s, discount factor Î³, and state value estimation function v_est. v_est must be a function that takes the arguments (mdp, s, Î³) and produces a reward of the same type as Î³ function monte_carlo_tree_search(mdp::MDP{S, A, F, G, H}, Î³::T, v_est::Function, s::S; depth = 10, nsims = 100, c = one(T), # visit_counts = Dict{S, SparseVector{T, Int64}}(), visit_counts = Dict{S, Dict{Int64, T}}(), # Q = Dict{S, SparseVector{T, Int64}}(), Q = Dict{S, Dict{Int64, T}}(), update_tree_policy! = (v, s) -> make_greedy_policy!(v), v_hold = zeros(T, length(mdp.actions)), updateQ! = function(Q, x, s, i_a) # Q[s][i_a] += x Q[s][i_a] = get_dict_value(Q[s], i_a) + x end, updateV! = function(V, x, s, i_a) # V[s][i_a] += x V[s][i_a] = get_dict_value(V[s], i_a) + x end, apply_bonus! = apply_uct!, make_step_kwargs = k -> NamedTuple(), #option to create mdp step arguments that depend on the simulation number, make_est_kwargs = k -> NamedTuple(), #option to create state estimation arguments that depend on the simulation number sim_message = false ) where {S, A, F, G, H, T<:Real} q_hold = zeros(T, length(mdp.actions)) #I want to have a way of possible a kwargs such as the answer index to the simulator that can change with each simulation t = time() last_time = t for k in 1:nsims seed = rand(UInt64) if sim_message elapsed = time() - last_time if elapsed > 5 last_time = time() pct_done = k/nsims total_time = time() - t ett = total_time / pct_done eta = ett - total_time @info """Completed simulation $k of $nsims after $(round(Int64, total_time/60)) minutes ETA: $(round(Int64, eta/60)) minutes""" end end simulate!(visit_counts, Q, mdp, Î³, v_est, s, depth, c, v_hold, update_tree_policy!, updateQ!, updateV!, q_hold, apply_bonus!, make_step_kwargs(seed), make_est_kwargs(seed)) end minv = minimum(Q[s][k] for k in keys(Q[s])) # v_hold .= Q[s] for i in eachindex(v_hold) v_hold[i] = get_dict_value(Q[s], i; default = minv) end make_greedy_policy!(v_hold) if sim_message @info "Finished MCTS evaluation of state $s" end return mdp.actions[sample_action(v_hold)], visit_counts, Q end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$dff6f326-ab7e-45e5-8c5e-28bfbb1d99bc„§cell_idÙ$dff6f326-ab7e-45e5-8c5e-28bfbb1d99bc¤codeÙÕmd""" ### Example 8.4: Prioritized Sweeping on Mazes Consider different mazes with the same wall structure as the Dyna Maze example but scaled up to different resolutions. Some example mazes are shown below. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$81f2f335-6606-4506-bfb3-d0d95e651f24„§cell_idÙ$81f2f335-6606-4506-bfb3-d0d95e651f24¤codeÚfunction bellman_optimal_update!(Q::Matrix{T}, i_s::Int64, i_a::Int64, mdp::FiniteStochasticMDP{T, S, A}, Î³::T) where {T <: Real, S, A} #perform a bellman optimal update for a given state action pair index and return the percentage change in value q_avg = zero(T) r_avg = zero(T) ptf = mdp.ptf[(i_a, i_s)] x = zero(T) for i_sâ€² in keys(ptf) (p, r) = ptf[i_sâ€²] maxvalue = typemin(T) @inbounds @fastmath @simd for i_aâ€² in eachindex(mdp.actions) maxvalue = max(maxvalue, Q[i_aâ€², i_sâ€²]) end x += p*(r + Î³ * maxvalue) end delt = abs(x - Q[i_a, i_s]) / (eps(abs(Q[i_a, i_s])) + abs(Q[i_a, i_s])) Q[i_a, i_s] = x return delt end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$aa898360-f802-438a-9081-a2e517230db2„§cell_idÙ$aa898360-f802-438a-9081-a2e517230db2¤codeÚÓfunction apply_uct!(v_hold::Vector{T}, q_ests::Dict{S, Dict{Int64, T}}, counts::Dict{S, Dict{Int64, T}}, s::S, c::T) where {S, T<:Real} ntot = sum(values(counts[s])) #for normal UCB selection, unvisited states have an infinite bonus v_hold .= T(Inf) @inbounds @fastmath for i in keys(q_ests[s]) #note that the only bonus values computed here are for actions that have been visited v_hold[i] = q_ests[s][i] + c * uct(counts, s, i, ntot) end return v_hold end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$2c587d5b-7b62-4835-ad02-9575c13d5874„§cell_idÙ$2c587d5b-7b62-4835-ad02-9575c13d5874¤codeÚøfunction show_mcts_solution() # visit_counts = Dict{GridworldState, SparseVector{Float32, Int64}}() visit_counts = Dict{GridworldState, Dict{Int64, Float32}}() # Q = Dict{GridworldState, SparseVector{Float32, Int64}}() Q = Dict{GridworldState, Dict{Int64, Float32}}() plot_path(dyna_maze, s -> monte_carlo_tree_search(dyna_maze, 0.95f0, rollout(;max_steps = 100), s; nsims = 100, depth = 100, c = 1.0f0, visit_counts = visit_counts, Q = Q)[1]; max_steps = 1000, iswall = s -> in(s, maze_walls)) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$a8ec05ad-8333-4423-ab42-883ab806ebd7„§cell_idÙ$a8ec05ad-8333-4423-ab42-883ab806ebd7¤codeÚ^function uniform_bellman_optimal_value!(Q::Matrix{T}, mdp::FiniteStochasticMDP{T, S, A}, Î³::T) where {T <: Real, S, A} delt = zero(T) num_updates = 0 for i_s in eachindex(mdp.states) for i_a in eachindex(mdp.actions) delt = max(delt, bellman_optimal_update!(Q, i_s, i_a, mdp, Î³)) num_updates += 1 end end return delt, num_updates end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂ«notebook_idÙ$3ee53c46-38f7-11f0-16c9-2b093684d981«in_temp_dirÂ¨metadata€