From ae26efd3ad46cc227b240f0c430d835102b782ad Mon Sep 17 00:00:00 2001 From: ffelten Date: Wed, 16 Oct 2024 12:45:33 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Farama-F?= =?UTF-8?q?oundation/MO-Gymnasium@d4d81ca8875bfde5e3f9960757831552738ea12e?= =?UTF-8?q?=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .buildinfo | 2 +- 404.html | 14 +- README/index.html | 14 +- _images/minecart-rgb.gif | Bin 0 -> 7198693 bytes _images/mo-lunar-lander-continuous.gif | Bin 0 -> 679945 bytes _static/basic.css | 2 +- _static/doctools.js | 2 +- _static/documentation_options.js | 2 +- _static/language_data.js | 4 +- _static/pygments.css | 12 +- _static/searchtools.js | 170 +++-- _static/versioning/versioning_menu.html | 38 +- _static/videos/minecart-rgb.gif | Bin 0 -> 7198693 bytes _static/videos/mo-lunar-lander-continuous.gif | Bin 0 -> 679945 bytes citing/citing/index.html | 10 +- community/community/index.html | 14 +- environments/breakable-bottles/index.html | 38 +- environments/classical/index.html | 34 +- .../deep-sea-treasure-concave/index.html | 31 +- .../deep-sea-treasure-mirrored/index.html | 31 +- environments/deep-sea-treasure/index.html | 31 +- environments/fishwood/index.html | 38 +- environments/four-room/index.html | 33 +- environments/fruit-tree/index.html | 27 +- environments/grid-world/index.html | 12 +- .../minecart-deterministic/index.html | 63 +- environments/minecart-rgb/index.html | 51 +- environments/minecart/index.html | 37 +- environments/misc/index.html | 18 +- environments/mo-ant-2d/index.html | 22 +- environments/mo-ant/index.html | 22 +- environments/mo-halfcheetah/index.html | 22 +- environments/mo-highway-fast/index.html | 15 +- environments/mo-highway/index.html | 21 +- environments/mo-hopper-2d/index.html | 22 +- environments/mo-hopper/index.html | 22 +- environments/mo-humanoid/index.html | 19 +- .../mo-lunar-lander-continuous/index.html | 45 +- environments/mo-lunar-lander/index.html | 31 +- environments/mo-mountaincar-3d/index.html | 662 ++++++++++++++++ .../mo-mountaincar-timemove/index.html | 662 ++++++++++++++++ .../mo-mountaincar-timespeed/index.html | 662 ++++++++++++++++ environments/mo-mountaincar/index.html | 25 +- .../mo-mountaincarcontinuous/index.html | 17 +- environments/mo-reacher/index.html | 25 +- environments/mo-supermario/index.html | 21 +- environments/mo-swimmer/index.html | 22 +- environments/mo-walker2d/index.html | 19 +- environments/mujoco/index.html | 12 +- environments/resource-gathering/index.html | 29 +- environments/water-reservoir/index.html | 29 +- examples/citation/index.html | 12 +- examples/morl_baselines/index.html | 18 +- examples/publications/index.html | 12 +- genindex/index.html | 26 +- index.html | 21 +- introduction/api/index.html | 14 +- introduction/install/index.html | 12 +- objects.inv | Bin 1385 -> 1521 bytes release_notes/index.html | 84 ++- search/index.html | 10 +- searchindex.js | 2 +- tutorials/custom_env/index.html | 20 +- wrappers/vector_wrappers/index.html | 713 ++++++++++++++++++ wrappers/wrappers/index.html | 106 +-- 65 files changed, 3664 insertions(+), 540 deletions(-) create mode 100644 _images/minecart-rgb.gif create mode 100644 _images/mo-lunar-lander-continuous.gif create mode 100644 _static/videos/minecart-rgb.gif create mode 100644 _static/videos/mo-lunar-lander-continuous.gif create mode 100644 environments/mo-mountaincar-3d/index.html create mode 100644 environments/mo-mountaincar-timemove/index.html create mode 100644 environments/mo-mountaincar-timespeed/index.html create mode 100644 wrappers/vector_wrappers/index.html diff --git a/.buildinfo b/.buildinfo index e1801f3b..055f7d26 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 3d61a9c2c162a7ac452c7ec2cbffeab5 +config: 9e28e034788f88155ad752a56985e689 tags: d77d1c0d9ca2f4c8421862c7c5a0d620 diff --git a/404.html b/404.html index e8fdd53e..a34feb5b 100644 --- a/404.html +++ b/404.html @@ -11,7 +11,7 @@ - + 404 - Page Not Found - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,9 +330,9 @@
    -

    404 - Page Not Found#

    +

    404 - Page Not Found

    -

    The requested page could not be found.#

    +

    The requested page could not be found.

    @@ -415,8 +419,8 @@

    The requested page could not be found. - + + diff --git a/README/index.html b/README/index.html index e4efb355..7fb6c104 100644 --- a/README/index.html +++ b/README/index.html @@ -11,7 +11,7 @@ - + MO-Gymnasium documentation - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,11 +330,11 @@
    -

    MO-Gymnasium documentation#

    +

    MO-Gymnasium documentation

    This folder contains the documentation for MO-Gymnasium.

    For more information about how to contribute to the documentation go to our CONTRIBUTING.md

    -

    Build the Documentation#

    +

    Build the Documentation

    Install the required packages and Gymnasium (or your fork):

    pip install -r docs/requirements.txt
     pip install -e .
    @@ -432,8 +436,8 @@ 

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zNb9!XHnkIW=keQO6OjPClOWMaz|#SS4;SEqgmb?9#yj`hcM6sEN>9jsXWs z6A8XP!?P2h#jO;u*#F5#yOI;xm te{C3kA@8i0aIdz_p6HMD%_?uqg%cm#N#LnbeB93J;&@+{2FRi<{{tD)i%kFk literal 0 HcmV?d00001 diff --git a/_static/basic.css b/_static/basic.css index 30fee9d0..f316efcb 100644 --- a/_static/basic.css +++ b/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ diff --git a/_static/doctools.js b/_static/doctools.js index d06a71d7..4d67807d 100644 --- a/_static/doctools.js +++ b/_static/doctools.js @@ -4,7 +4,7 @@ * * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ diff --git a/_static/documentation_options.js b/_static/documentation_options.js index 6fe7b331..59816731 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,5 +1,5 @@ const DOCUMENTATION_OPTIONS = { - VERSION: '1.1.0', + VERSION: '1.2.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'dirhtml', diff --git a/_static/language_data.js b/_static/language_data.js index 250f5665..367b8ed8 100644 --- a/_static/language_data.js +++ b/_static/language_data.js @@ -5,7 +5,7 @@ * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -13,7 +13,7 @@ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; -/* Non-minified version is copied as a separate JS file, is available */ +/* Non-minified version is copied as a separate JS file, if available */ /** * Porter Stemmer diff --git a/_static/pygments.css b/_static/pygments.css index c2e07c71..02b4b128 100644 --- a/_static/pygments.css +++ b/_static/pygments.css @@ -106,17 +106,17 @@ body[data-theme="dark"] .highlight .cp { color: #ff3a3a; font-weight: bold } /* body[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */ +body[data-theme="dark"] .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ body[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body[data-theme="dark"] .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ -body[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */ +body[data-theme="dark"] .highlight .gr { color: #ff3a3a } /* Generic.Error */ body[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */ body[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */ body[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */ body[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */ body[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */ body[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */ -body[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */ +body[data-theme="dark"] .highlight .gt { color: #ff3a3a } /* Generic.Traceback */ body[data-theme="dark"] .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */ body[data-theme="dark"] .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */ body[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */ @@ -192,17 +192,17 @@ body:not([data-theme="light"]) .highlight .cp { color: #ff3a3a; font-weight: bol body:not([data-theme="light"]) .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body:not([data-theme="light"]) .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body:not([data-theme="light"]) .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body:not([data-theme="light"]) .highlight .gd { color: #d22323 } /* Generic.Deleted */ +body:not([data-theme="light"]) .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ body:not([data-theme="light"]) .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body:not([data-theme="light"]) .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ -body:not([data-theme="light"]) .highlight .gr { color: #d22323 } /* Generic.Error */ +body:not([data-theme="light"]) .highlight .gr { color: #ff3a3a } /* Generic.Error */ body:not([data-theme="light"]) .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */ body:not([data-theme="light"]) .highlight .gi { color: #589819 } /* Generic.Inserted */ body:not([data-theme="light"]) .highlight .go { color: #cccccc } /* Generic.Output */ body:not([data-theme="light"]) .highlight .gp { color: #aaaaaa } /* Generic.Prompt */ body:not([data-theme="light"]) .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */ body:not([data-theme="light"]) .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */ -body:not([data-theme="light"]) .highlight .gt { color: #d22323 } /* Generic.Traceback */ +body:not([data-theme="light"]) .highlight .gt { color: #ff3a3a } /* Generic.Traceback */ body:not([data-theme="light"]) .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */ body:not([data-theme="light"]) .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */ body:not([data-theme="light"]) .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */ diff --git a/_static/searchtools.js b/_static/searchtools.js index 7918c3fa..b08d58c9 100644 --- a/_static/searchtools.js +++ b/_static/searchtools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for the full-text search. * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -99,7 +99,7 @@ const _displayItem = (item, searchTerms, highlightTerms) => { .then((data) => { if (data) listItem.appendChild( - Search.makeSearchSummary(data, searchTerms) + Search.makeSearchSummary(data, searchTerms, anchor) ); // highlight search terms in the summary if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js @@ -116,8 +116,8 @@ const _finishSearch = (resultCount) => { ); else Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); }; const _displayNextItem = ( results, @@ -137,6 +137,22 @@ const _displayNextItem = ( // search finished, update title and status message else _finishSearch(resultCount); }; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; /** * Default splitQuery function. Can be overridden in ``sphinx.search`` with a @@ -160,13 +176,26 @@ const Search = { _queued_query: null, _pulse_status: -1, - htmlToText: (htmlString) => { + htmlToText: (htmlString, anchor) => { const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; + if (docContent) return docContent.textContent; + console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." ); return ""; }, @@ -239,16 +268,7 @@ const Search = { else Search.deferQuery(query); }, - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - + _parseQuery: (query) => { // stem the search terms and add them to the correct list const stemmer = new Stemmer(); const searchTerms = new Set(); @@ -284,21 +304,38 @@ const Search = { // console.info("required: ", [...searchTerms]); // console.info("excluded: ", [...excludedTerms]); - // array of [docname, title, anchor, descr, score, filename] - let results = []; + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + _removeChildren(document.getElementById("search-progress")); - const queryLower = query.toLowerCase(); + const queryLower = query.toLowerCase().trim(); for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ docNames[file], titles[file] !== title ? `${titles[file]} > ${title}` : title, id !== null ? "#" + id : "", null, - score, + score + boost, filenames[file], ]); } @@ -308,46 +345,47 @@ const Search = { // search for explicit entries in index directives for (const [entry, foundEntries] of Object.entries(indexEntries)) { if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ docNames[file], titles[file], id ? "#" + id : "", null, score, filenames[file], - ]); + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } } } } // lookup as object objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) + normalResults.push(...Search.performObjectSearch(term, objectTerms)) ); // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; // remove duplicate search results // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept @@ -361,7 +399,12 @@ const Search = { return acc; }, []); - results = results.reverse(); + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); // for debugging //Search.lastresults = results.slice(); // a copy @@ -466,14 +509,18 @@ const Search = { // add support for partial matches if (word.length > 2) { const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } } // no match but word was a required one @@ -496,9 +543,8 @@ const Search = { // create the mapping files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); }); }); @@ -549,8 +595,8 @@ const Search = { * search summary for a given text. keywords is a list * of stemmed words. */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); if (text === "") return null; const textLower = text.toLowerCase(); diff --git a/_static/versioning/versioning_menu.html b/_static/versioning/versioning_menu.html index 1daf4cef..68109cf8 100644 --- a/_static/versioning/versioning_menu.html +++ b/_static/versioning/versioning_menu.html @@ -122,15 +122,33 @@ }); const sortVersions = (a, b) => { - // Alpha versions - if (a.includes("a")) return 1; - if (b.includes("a")) return -1; - if (a.replace("v", "") > b.replace("v", "")) - return -1 - else if (a.replace("v", "") < b.replace("v", "")) - return 1 - else - return 0 + const innerA = a.replace("v", ""); + const innerB = b.replace("v", ""); + + if (innerA.match(/[a-z]/) && !innerB.match(/[a-z]/)) return 1; + if (!innerA.match(/[a-z]/) && innerB.match(/[a-z]/)) return -1; + + const splittedInnerA = innerA.split(""); + const splittedInnerB = innerB.split(""); + + while (splittedInnerA.length && splittedInnerB.length) { + const charA = splittedInnerA.shift(); + const charB = splittedInnerB.shift(); + + if (charA === charB) continue; + else if (charA === ".") return 1; + else if (charB === ".") return -1; + else if (charA.match(/[0-9]/) && charB.match(/[0-9]/)) { + return Number(charB) - Number(charA); + } else { + if (charA < charB) return 1; + else if (charA > charB) return -1; + else return 0; + } + } + if (innerA < innerB) return 1; + else if (innerA > innerB) return -1; + else return 0; } if ((githubUser !== null && githubUser !== "") || (repo !== null && repo !== "")) { @@ -204,4 +222,4 @@ console.error("Invalid versioning configuration"); } -

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  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Install
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  • Miscellaneous @@ -401,8 +405,8 @@ - - + + diff --git a/community/community/index.html b/community/community/index.html index 5ac06a12..c1a5ea80 100644 --- a/community/community/index.html +++ b/community/community/index.html @@ -11,7 +11,7 @@ - + Community - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
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  • Miscellaneous @@ -326,10 +330,10 @@
    -

    Community#

    +

    Community

    If you want to help us out, reach us, or simply ask questions, you can join the Farama discord server here.

    -

    Acknowledgements#

    +

    Acknowledgements

    Aside from the main contributors, some people have also contributed to the project in various ways. We would like to thank them all for their contributions.

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Breakable-Bottles#

    +

    Breakable-Bottles

    -../../_images/breakable-bottles.gif +../../_images/breakable-bottles.gif +
    @@ -333,7 +338,7 @@

    Breakable-Bottles

    - + @@ -351,12 +356,12 @@

    Breakable-Bottles -

    Description#

    +

    Description

    This environment implements the problems UnbreakableBottles and BreakableBottles defined in Section 4.1.2 of the paper Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety.

    -

    Action Space#

    +

    Action Space

    The action space is a discrete space with 3 actions:

    • 0: move left

    • @@ -365,17 +370,20 @@

      Action Space -

      Observation Space#

      +

      Observation Space

      The observation space is a dictionary with 4 keys:

      • location: the current location of the agent

      • bottles_carrying: the number of bottles the agent is currently carrying (0, 1 or 2)

      • -
      • bottles_delivered: the number of bottles the agent has delivered (0 or 1)

      • +
      • bottles_delivered: the number of bottles the agent has delivered (0, 1 or 2)

      • bottles_dropped: for each location, a boolean flag indicating if that location currently contains a bottle

      +

      Note that this observation space is different from that listed in the paper above. In the paper, bottles_delivered’s possible values are listed as (0 or 1), +rather than (0, 1 or 2). This is because the paper did not take the terminal state, in which 2 bottles have been delivered, into account when calculating +the observation space. As such, the observation space of this implementation is larger than specified in the paper, having 360 possible states instead of 240.

    -

    Reward Space#

    +

    Reward Space

    The reward space has 3 dimensions:

    • time penalty: -1 for each time step

    • @@ -384,15 +392,15 @@

      Reward Space -

      Starting State#

      +

      Starting State

      The agent starts at location 0, carrying no bottles, having delivered no bottles and having dropped no bottles.

    -

    Episode Termination#

    +

    Episode Termination

    The episode terminates when the agent has delivered 2 bottles.

    -

    Arguments#

    +

    Arguments

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,7 +330,7 @@
    -

    Classic Control#

    +

    Classic Control

    Multi-objective versions of classical Gymnasium’s environments.

  • Observation Space

    Dict(‘bottles_carrying’: Discrete(3), ‘bottles_delivered’: Discrete(2), ‘bottles_dropped’: MultiBinary(3), ‘location’: Discrete(5))

    Dict(‘bottles_carrying’: Discrete(3), ‘bottles_delivered’: Discrete(3), ‘bottles_dropped’: MultiBinary(3), ‘location’: Discrete(5))

    Reward Shape

    (3,)

    @@ -343,12 +347,27 @@

    Classic Control[time_penalty, reverse_penalty, forward_penalty]

    - + + + + + + + + + + + + + + + + - + @@ -356,6 +375,9 @@

    Classic ControlPranav Gupta's Dissertation

    +
    @@ -446,8 +468,8 @@

    Classic Control - + + diff --git a/environments/deep-sea-treasure-concave/index.html b/environments/deep-sea-treasure-concave/index.html index 151b812e..b9dc38bd 100644 --- a/environments/deep-sea-treasure-concave/index.html +++ b/environments/deep-sea-treasure-concave/index.html @@ -11,7 +11,7 @@ - + Deep-Sea-Treasure-Concave - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Deep-Sea-Treasure-Concave#

    +

    Deep-Sea-Treasure-Concave

    -../../_images/deep-sea-treasure-concave.gif +../../_images/deep-sea-treasure-concave.gif +
  • Classic Mountain Car env, but with extra penalties for the forward and reverse actions. From Vamplew et al. 2011.

    mo-mountaincarcontinuous-v0

    mo-mountaincar-3d-v0 **

    Continuous / Discrete

    [time_penalty, move_penalty, speed_objective]

    The forward and backward penalties have been merged into the move penalty and a speed objective has been introduced which gives the positive reward equivalent to the car’s speed at that time step.*

    mo-mountaincar-timemove-v0 **

    Continuous / Discrete

    [time_penalty, move_penalty]

    Class Mountain Car env but an extra penalty for moving backwards or forwards merged into a move penalty.

    mo-mountaincar-timespeed-v0 **

    Continuous / Discrete

    [time_penalty, speed_objective]

    Class Mountain Car env but an extra positive objective of speed which gives the positive reward equivalent to the car’s speed at that time step.*

    mo-mountaincarcontinuous-v0

    Continuous / Continuous

    [time_penalty, fuel_consumption_penalty]

    Continuous Mountain Car env, but with penalties for fuel consumption.

    mo-lunar-lander-v2

    mo-lunar-lander-v2

    Continuous / Discrete or Continuous

    [landed, shaped_reward, main_engine_fuel, side_engine_fuel]

    MO version of the LunarLander-v2 environment. Objectives defined similarly as in Hung et al. 2022.

    @@ -357,15 +362,15 @@

    Deep-Sea-Treasure-Concave -

    Description#

    +

    Description

    The Deep Sea Treasure environment is classic MORL problem in which the agent controls a submarine in a 2D grid world.

    -

    Observation Space#

    +

    Observation Space

    The observation space is a 2D discrete box with values in [0, 10] for the x and y coordinates of the submarine.

    -

    Action Space#

    +

    Action Space

    The actions is a discrete space where:

    • 0: up

    • @@ -375,7 +380,7 @@

      Action Space -

      Reward Space#

      +

      Reward Space

      The reward is 2-dimensional:

      • time penalty: -1 at each time step

      • @@ -383,22 +388,22 @@

        Reward Space -

        Starting State#

        +

        Starting State

        The starting state is always the same: (0, 0)

    -

    Episode Termination#

    +

    Episode Termination

    The episode terminates when the agent reaches a treasure.

    -

    Arguments#

    +

    Arguments

    • dst_map: the map of the deep sea treasure. Default is the convex map from Yang et al. (2019). To change, use mo_gymnasium.make("DeepSeaTreasure-v0", dst_map=CONCAVE_MAP | MIRRORED_MAP).

    • float_state: if True, the state is a 2D continuous box with values in [0.0, 1.0] for the x and y coordinates of the submarine.

    -

    Credits#

    +

    Credits

    The code was adapted from: Yang’s source. The background art is from https://ansimuz.itch.io/underwater-fantasy-pixel-art-environment. The submarine art was created with the assistance of DALL·E 2.

    @@ -517,8 +522,8 @@

    Credits#< - - + + diff --git a/environments/deep-sea-treasure-mirrored/index.html b/environments/deep-sea-treasure-mirrored/index.html index 26df938a..4ff35b44 100644 --- a/environments/deep-sea-treasure-mirrored/index.html +++ b/environments/deep-sea-treasure-mirrored/index.html @@ -11,7 +11,7 @@ - + Deep-Sea-Treasure-Mirrored - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Deep-Sea-Treasure-Mirrored#

    +

    Deep-Sea-Treasure-Mirrored

    -../../_images/deep-sea-treasure-mirrored.gif +../../_images/deep-sea-treasure-mirrored.gif +
  • @@ -357,15 +362,15 @@

    Deep-Sea-Treasure-Mirrored -

    Description#

    +

    Description

    The Deep Sea Treasure environment is classic MORL problem in which the agent controls a submarine in a 2D grid world.

    -

    Observation Space#

    +

    Observation Space

    The observation space is a 2D discrete box with values in [0, 10] for the x and y coordinates of the submarine.

    -

    Action Space#

    +

    Action Space

    The actions is a discrete space where:

    • 0: up

    • @@ -375,7 +380,7 @@

      Action Space -

      Reward Space#

      +

      Reward Space

      The reward is 2-dimensional:

      • time penalty: -1 at each time step

      • @@ -383,22 +388,22 @@

        Reward Space -

        Starting State#

        +

        Starting State

        The starting state is always the same: (0, 0)

    -

    Episode Termination#

    +

    Episode Termination

    The episode terminates when the agent reaches a treasure.

    -

    Arguments#

    +

    Arguments

    • dst_map: the map of the deep sea treasure. Default is the convex map from Yang et al. (2019). To change, use mo_gymnasium.make("DeepSeaTreasure-v0", dst_map=CONCAVE_MAP | MIRRORED_MAP).

    • float_state: if True, the state is a 2D continuous box with values in [0.0, 1.0] for the x and y coordinates of the submarine.

    -

    Credits#

    +

    Credits

    The code was adapted from: Yang’s source. The background art is from https://ansimuz.itch.io/underwater-fantasy-pixel-art-environment. The submarine art was created with the assistance of DALL·E 2.

    @@ -507,8 +512,8 @@

    Credits#< - - + + diff --git a/environments/deep-sea-treasure/index.html b/environments/deep-sea-treasure/index.html index 9a4f7271..37335e52 100644 --- a/environments/deep-sea-treasure/index.html +++ b/environments/deep-sea-treasure/index.html @@ -11,7 +11,7 @@ - + Deep-Sea-Treasure - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Deep-Sea-Treasure#

    +

    Deep-Sea-Treasure

    -../../_images/deep-sea-treasure.gif +../../_images/deep-sea-treasure.gif +
  • @@ -357,15 +362,15 @@

    Deep-Sea-Treasure -

    Description#

    +

    Description

    The Deep Sea Treasure environment is classic MORL problem in which the agent controls a submarine in a 2D grid world.

    -

    Observation Space#

    +

    Observation Space

    The observation space is a 2D discrete box with values in [0, 10] for the x and y coordinates of the submarine.

    -

    Action Space#

    +

    Action Space

    The actions is a discrete space where:

    • 0: up

    • @@ -375,7 +380,7 @@

      Action Space -

      Reward Space#

      +

      Reward Space

      The reward is 2-dimensional:

      • time penalty: -1 at each time step

      • @@ -383,22 +388,22 @@

        Reward Space -

        Starting State#

        +

        Starting State

        The starting state is always the same: (0, 0)

    -

    Episode Termination#

    +

    Episode Termination

    The episode terminates when the agent reaches a treasure.

    -

    Arguments#

    +

    Arguments

    • dst_map: the map of the deep sea treasure. Default is the convex map from Yang et al. (2019). To change, use mo_gymnasium.make("DeepSeaTreasure-v0", dst_map=CONCAVE_MAP | MIRRORED_MAP).

    • float_state: if True, the state is a 2D continuous box with values in [0.0, 1.0] for the x and y coordinates of the submarine.

    -

    Credits#

    +

    Credits

    The code was adapted from: Yang’s source. The background art is from https://ansimuz.itch.io/underwater-fantasy-pixel-art-environment. The submarine art was created with the assistance of DALL·E 2.

    @@ -505,8 +510,8 @@

    Credits#< - - + + diff --git a/environments/fishwood/index.html b/environments/fishwood/index.html index 4acbfed0..389cffcf 100644 --- a/environments/fishwood/index.html +++ b/environments/fishwood/index.html @@ -11,7 +11,7 @@ - + Fishwood - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,7 +321,7 @@
    -

    Fishwood#

    +

    Fishwood

  • @@ -329,8 +333,14 @@

    Fishwood

    - - + + + + + + + + @@ -348,12 +358,12 @@

    Fishwood

    Action Space

    Discrete(2)

    Observation Space

    Discrete(2)

    Observation Shape

    (1,)

    Observation High

    [1]

    Observation Low

    [0]

    Reward Shape

    (2,)

    -

    Description#

    +

    Description

    The FishWood environment is a simple MORL problem in which the agent controls a fisherman which can either fish or go collect wood. From Multi-objective Reinforcement Learning for the Expected Utility of the Return.

    -

    Observation Space#

    +

    Observation Space

    The observation space is a discrete space with two states:

    • 0: fishing

    • @@ -361,7 +371,7 @@

      Observation Space -

      Action Space#

      +

      Action Space

      The actions is a discrete space where:

      • 0: go fishing

      • @@ -369,7 +379,7 @@

        Action Space -

        Reward Space#

        +

        Reward Space

        The reward is 2-dimensional:

        • 0: +1 if agent is in the woods, with woodproba probability, and 0 otherwise

        • @@ -377,22 +387,22 @@

          Reward Space -

          Starting State#

          +

          Starting State

          Agent starts in the woods

    -

    Termination#

    +

    Termination

    The episode ends after MAX_TS=200 steps

    -

    Arguments#

    +

    Arguments

    • fishproba: probability of catching a fish when fishing

    • woodproba: probability of collecting wood when in the woods

    -

    Credits#

    +

    Credits

    Code provided by Denis Steckelmacher

    @@ -497,8 +507,8 @@

    Credits#< - - + + diff --git a/environments/four-room/index.html b/environments/four-room/index.html index b246ebc8..19e87389 100644 --- a/environments/four-room/index.html +++ b/environments/four-room/index.html @@ -11,7 +11,7 @@ - + Four-Room - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Four-Room#

    +

    Four-Room

    -../../_images/four-room.gif +../../_images/four-room.gif +
    @@ -357,25 +362,25 @@

    Four-Room -

    Description#

    +

    Description

    A discretized version of the gridworld environment introduced in [1]. Here, an agent learns to collect shapes with positive reward, while avoid those with negative reward, and then travel to a fixed goal. The gridworld is split into four rooms separated by walls with passage-ways.

    -

    References#

    +

    References

    [1] Barreto, André, et al. “Successor Features for Transfer in Reinforcement Learning.” NIPS. 2017.

    -

    Observation Space#

    +

    Observation Space

    The observation contains the 2D position of the agent in the gridworld, plus a binary vector indicating which items were collected.

    -

    Action Space#

    +

    Action Space

    The action space is discrete with 4 actions: left, up, right, down.

    -

    Reward Space#

    +

    Reward Space

    The reward is a 3-dimensional vector with the following components:

    • +1 if collected a blue square, else 0

    • @@ -384,21 +389,21 @@

      Reward Space -

      Starting State#

      +

      Starting State

      The agent starts in the lower left of the map.

    -

    Episode Termination#

    +

    Episode Termination

    The episode terminates when the agent reaches the goal state, G.

    -

    Arguments#

    +

    Arguments

    • maze: Array containing the gridworld map. See MAZE for an example.

    -

    Credits#

    +

    Credits

    Code adapted from: Mike Gimelfarb’s source.

    @@ -504,8 +509,8 @@

    Credits#< - - + + diff --git a/environments/fruit-tree/index.html b/environments/fruit-tree/index.html index 823036ca..413e6361 100644 --- a/environments/fruit-tree/index.html +++ b/environments/fruit-tree/index.html @@ -11,7 +11,7 @@ - + Fruit-Tree - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Fruit-Tree#

    +

    Fruit-Tree

    -../../_images/fruit-tree.gif +../../_images/fruit-tree.gif +
  • @@ -357,28 +362,28 @@

    Fruit-Tree -

    Description#

    +

    Description

    Full binary tree of depth d=5,6 or 7. Every leaf contains a fruit with a value for the nutrients Protein, Carbs, Fats, Vitamins, Minerals and Water. From Yang et al. 2019.

    -

    Observation Space#

    +

    Observation Space

    Discrete space of size 2^d-1, where d is the depth of the tree.

    -

    Action Space#

    +

    Action Space

    The agent can chose to go left or right at every node. The action space is therefore a discrete space of size 2.

    -

    Reward Space#

    +

    Reward Space

    Each leaf node contains a 6-dimensional vector containing the nutrients of the fruit. The agent receives a reward for each nutrient it collects.

    -

    Starting State#

    +

    Starting State

    The agent starts at the root node (0, 0).

    -

    Episode Termination#

    +

    Episode Termination

    The episode terminates when the agent reaches a leaf node.

    @@ -481,8 +486,8 @@

    Episode Termination - + + diff --git a/environments/grid-world/index.html b/environments/grid-world/index.html index c5a08d43..4a1e46d8 100644 --- a/environments/grid-world/index.html +++ b/environments/grid-world/index.html @@ -11,7 +11,7 @@ - + Grid-World - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,7 +330,7 @@
    -

    Grid-World#

    +

    Grid-World

    Environments with discrete observation spaces, e.g., grid-worlds.

  • @@ -471,8 +475,8 @@

    Grid-World - + + diff --git a/environments/minecart-deterministic/index.html b/environments/minecart-deterministic/index.html index 12f4babb..4b865583 100644 --- a/environments/minecart-deterministic/index.html +++ b/environments/minecart-deterministic/index.html @@ -8,10 +8,10 @@ - + - + Minecart-Deterministic - MO-Gymnasium Documentation @@ -215,10 +215,11 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    -

    @@ -357,12 +362,12 @@

    Minecart-Deterministic
    -

    Description#

    +

    Description

    Agent must collect two types of ores and minimize fuel consumption. From Abels et al. 2019.

    -

    Observation Space#

    +

    Observation Space

    The observation is a 7-dimensional vector containing the following information:

    • 2D position of the cart

    • @@ -373,7 +378,7 @@

      Observation Space -

      Action Space#

      +

      Action Space

      The action space is a discrete space with 6 actions:

      • 0: Mine

      • @@ -385,7 +390,7 @@

        Action Space -

        Reward Space#

        +

        Reward Space

        The reward is a 3D vector:

        • 0: Quantity of the first minerium that was retrieved to the base (sparse)

        • @@ -394,15 +399,15 @@

          Reward Space -

          Starting State#

          +

          Starting State

          The cart starts at the base on the upper left corner of the map.

    -

    Episode Termination#

    +

    Episode Termination

    The episode ends when the cart returns to the base.

    -

    Arguments#

    +

    Arguments

    • render_mode: The render mode to use. Can be “rgb_array” or “human”.

    • image_observation: If True, the observation is a RGB image of the environment.

    • @@ -412,7 +417,7 @@

      Arguments -

      Credits#

      +

      Credits

      The code was refactored from Axel Abels’ source.

    @@ -422,8 +427,30 @@

    Credits#<

    @@ -357,12 +362,12 @@

    Minecart-Rgb -

    Description#

    +

    Description

    Agent must collect two types of ores and minimize fuel consumption. From Abels et al. 2019.

    -

    Observation Space#

    +

    Observation Space

    The observation is a 7-dimensional vector containing the following information:

    • 2D position of the cart

    • @@ -373,7 +378,7 @@

      Observation Space -

      Action Space#

      +

      Action Space

      The action space is a discrete space with 6 actions:

      • 0: Mine

      • @@ -385,7 +390,7 @@

        Action Space -

        Reward Space#

        +

        Reward Space

        The reward is a 3D vector:

        • 0: Quantity of the first minerium that was retrieved to the base (sparse)

        • @@ -394,15 +399,15 @@

          Reward Space -

          Starting State#

          +

          Starting State

          The cart starts at the base on the upper left corner of the map.

    -

    Episode Termination#

    +

    Episode Termination

    The episode ends when the cart returns to the base.

    -

    Arguments#

    +

    Arguments

    • render_mode: The render mode to use. Can be “rgb_array” or “human”.

    • image_observation: If True, the observation is a RGB image of the environment.

    • @@ -412,7 +417,7 @@

      Arguments -

      Credits#

      +

      Credits

      The code was refactored from Axel Abels’ source.

    @@ -423,7 +428,19 @@

    Credits#<

    @@ -357,12 +362,12 @@

    Minecart

    -

    Description#

    +

    Description

    Agent must collect two types of ores and minimize fuel consumption. From Abels et al. 2019.

    -

    Observation Space#

    +

    Observation Space

    The observation is a 7-dimensional vector containing the following information:

    • 2D position of the cart

    • @@ -373,7 +378,7 @@

      Observation Space -

      Action Space#

      +

      Action Space

      The action space is a discrete space with 6 actions:

      • 0: Mine

      • @@ -385,7 +390,7 @@

        Action Space -

        Reward Space#

        +

        Reward Space

        The reward is a 3D vector:

        • 0: Quantity of the first minerium that was retrieved to the base (sparse)

        • @@ -394,15 +399,15 @@

          Reward Space -

          Starting State#

          +

          Starting State

          The cart starts at the base on the upper left corner of the map.

    -

    Episode Termination#

    +

    Episode Termination

    The episode ends when the cart returns to the base.

    -

    Arguments#

    +

    Arguments

    • render_mode: The render mode to use. Can be “rgb_array” or “human”.

    • image_observation: If True, the observation is a RGB image of the environment.

    • @@ -412,7 +417,7 @@

      Arguments -

      Credits#

      +

      Credits

      The code was refactored from Axel Abels’ source.

    @@ -422,12 +427,12 @@

    Credits#<

  • Miscellaneous @@ -326,7 +330,7 @@
    -

    Miscellaneous#

    +

    Miscellaneous

    MO-Gymnasium also includes other miscellaneous multi-objective environments:

    @@ -381,7 +385,7 @@

    Miscellaneous - + @@ -390,7 +394,7 @@

    MiscellaneousMO-Lunar-Lander +
    MO-Lunar-Lander-Continuous
    @@ -451,8 +455,8 @@

    Miscellaneous - + + diff --git a/environments/mo-ant-2d/index.html b/environments/mo-ant-2d/index.html index b7a49d8b..62116594 100644 --- a/environments/mo-ant-2d/index.html +++ b/environments/mo-ant-2d/index.html @@ -11,7 +11,7 @@ - + MO-Ant-2D - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Ant-2D#

    +

    MO-Ant-2D

    -_static/videos/mo-ant-2d.gif +_static/videos/mo-ant-2d.gif +
  • @@ -357,12 +362,15 @@

    MO-Ant-2D -

    Description#

    +

    Description

    Multi-objective version of the AntEnv environment.

    See Gymnasium’s env for more information.

    +

    The original Gymnasium’s ‘Ant-v4’ is recovered by the following linear scalarization:

    +

    env = mo_gym.make(‘mo-ant-v4’, cost_objective=False) +LinearReward(env, weight=np.array([1.0, 0.0]))

    -

    Reward Space#

    +

    Reward Space

    The reward is 2- or 3-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Ant#

    +

    MO-Ant

    -../../_images/mo-ant.gif +../../_images/mo-ant.gif +
  • @@ -357,12 +362,15 @@

    MO-Ant#
    -

    Description#

    +

    Description

    Multi-objective version of the AntEnv environment.

    See Gymnasium’s env for more information.

    +

    The original Gymnasium’s ‘Ant-v4’ is recovered by the following linear scalarization:

    +

    env = mo_gym.make(‘mo-ant-v4’, cost_objective=False) +LinearReward(env, weight=np.array([1.0, 0.0]))

    -

    Reward Space#

    +

    Reward Space

    The reward is 2- or 3-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Halfcheetah#

    +

    MO-Halfcheetah

    -../../_images/mo-halfcheetah.gif +../../_images/mo-halfcheetah.gif +
  • @@ -357,12 +362,15 @@

    MO-Halfcheetah -

    Description#

    +

    Description

    Multi-objective version of the HalfCheetahEnv environment.

    See Gymnasium’s env for more information.

    +

    The original Gymnasium’s ‘HalfCheetah-v4’ is recovered by the following linear scalarization:

    +

    env = mo_gym.make(‘mo-halfcheetah-v4’) +LinearReward(env, weight=np.array([1.0, 1.0]))

    -

    Reward Space#

    +

    Reward Space

    The reward is 2-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Highway-Fast#

    +

    MO-Highway-Fast

    -../../_images/mo-highway-fast.gif +../../_images/mo-highway-fast.gif +
  • @@ -423,8 +428,8 @@

    MO-Highway-Fast - + + diff --git a/environments/mo-highway/index.html b/environments/mo-highway/index.html index 5996b505..19c60fda 100644 --- a/environments/mo-highway/index.html +++ b/environments/mo-highway/index.html @@ -8,10 +8,10 @@ - + - + MO-Highway - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Highway#

    +

    MO-Highway

    -../../_images/mo-highway.gif +../../_images/mo-highway.gif +
  • @@ -357,12 +362,12 @@

    MO-Highway -

    Description#

    +

    Description

    Multi-objective version of the HighwayEnv environment.

    See highway-env for more information.

    -

    Reward Space#

    +

    Reward Space

    The reward is 3-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Hopper-2D#

    +

    MO-Hopper-2D

    -_static/videos/mo-hopper-2d.gif +_static/videos/mo-hopper-2d.gif +
  • @@ -357,12 +362,15 @@

    MO-Hopper-2D -

    Description#

    +

    Description

    Multi-objective version of the HopperEnv environment.

    See Gymnasium’s env for more information.

    +

    The original Gymnasium’s ‘Hopper-v4’ is recovered by the following linear scalarization:

    +

    env = mo_gym.make(‘mo-hopper-v4’, cost_objective=False) +LinearReward(env, weight=np.array([1.0, 0.0]))

    -

    Reward Space#

    +

    Reward Space

    The reward is 3-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Hopper#

    +

    MO-Hopper

    -../../_images/mo-hopper.gif +../../_images/mo-hopper.gif +
  • @@ -357,12 +362,15 @@

    MO-Hopper -

    Description#

    +

    Description

    Multi-objective version of the HopperEnv environment.

    See Gymnasium’s env for more information.

    +

    The original Gymnasium’s ‘Hopper-v4’ is recovered by the following linear scalarization:

    +

    env = mo_gym.make(‘mo-hopper-v4’, cost_objective=False) +LinearReward(env, weight=np.array([1.0, 0.0]))

    -

    Reward Space#

    +

    Reward Space

    The reward is 3-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Humanoid#

    +

    MO-Humanoid

    -../../_images/mo-humanoid.gif +../../_images/mo-humanoid.gif +
  • @@ -357,12 +362,12 @@

    MO-Humanoid -

    Description#

    +

    Description

    Multi-objective version of the HumanoidEnv environment.

    See Gymnasium’s env for more information.

    -

    Reward Space#

    +

    Reward Space

    The reward is 2-dimensional:

    Environments

    -
    @@ -336,10 +341,10 @@

    MO-Lunar-Lander-Continuous

    - + - + @@ -351,18 +356,18 @@

    MO-Lunar-Lander-Continuous

    - +

    Observation High

    [1.5 1.5 5. 5. 3.14 5. 1. 1. ]

    [ 2.5 2.5 10. 10. 6.28 10. 1. 1. ]

    Observation Low

    [-1.5 -1.5 -5. -5. -3.14 -5. -0. -0. ]

    [ -2.5 -2.5 -10. -10. -6.28 -10. -0. -0. ]

    Reward Shape

    (4,)

    Import

    mo_gymnasium.make("mo-lunar-lander-continuous-v2")

    mo_gymnasium.make("mo-lunar-lander-continuous-v3")

    -

    Description#

    +

    Description

    Multi-objective version of the LunarLander environment.

    See Gymnasium’s env for more information.

    -

    Reward Space#

    +

    Reward Space

    The reward is 4-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Lunar-Lander#

    +

    MO-Lunar-Lander

    -../../_images/mo-lunar-lander.gif +../../_images/mo-lunar-lander.gif +
    @@ -336,10 +341,10 @@

    MO-Lunar-Lander

    - + - + @@ -351,18 +356,18 @@

    MO-Lunar-Lander

    - +

    Observation High

    [1.5 1.5 5. 5. 3.14 5. 1. 1. ]

    [ 2.5 2.5 10. 10. 6.28 10. 1. 1. ]

    Observation Low

    [-1.5 -1.5 -5. -5. -3.14 -5. -0. -0. ]

    [ -2.5 -2.5 -10. -10. -6.28 -10. -0. -0. ]

    Reward Shape

    (4,)

    Import

    mo_gymnasium.make("mo-lunar-lander-v2")

    mo_gymnasium.make("mo-lunar-lander-v3")

    -

    Description#

    +

    Description

    Multi-objective version of the LunarLander environment.

    See Gymnasium’s env for more information.

    -

    Reward Space#

    +

    Reward Space

    The reward is 4-dimensional:

    • 0: -100 if crash, +100 if lands successfully

    • @@ -378,12 +383,12 @@

      Reward Space - +
      Next
      -
      Miscellaneous
      +
      MO-Lunar-Lander-Continuous
      @@ -467,8 +472,8 @@

      Reward Space - + + diff --git a/environments/mo-mountaincar-3d/index.html b/environments/mo-mountaincar-3d/index.html new file mode 100644 index 00000000..35045fa1 --- /dev/null +++ b/environments/mo-mountaincar-3d/index.html @@ -0,0 +1,662 @@ + + + + + + + + + + + + + + + MO-Mountaincar-3D - MO-Gymnasium Documentation + + + + + + + + + +
      + +
      + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + +
      + + +
      + + + + + +
      +
      +
      + + + + + Back to top + +
      + +
      + +
      +
      + +
      +

      MO-Mountaincar-3D

      +
      +_static/videos/mo-mountaincar-3d.gif + +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      Action Space

      Discrete(3)

      Observation Shape

      (2,)

      Observation High

      [0.6 0.07]

      Observation Low

      [-1.2 -0.07]

      Reward Shape

      (3,)

      Reward High

      [-1. 0. 1.1]

      Reward Low

      [-1. -1. 0.]

      Import

      mo_gymnasium.make("mo-mountaincar-3d-v0")

      +
      +

      A multi-objective version of the MountainCar environment, where the goal is to reach the top of the mountain.

      +

      See Gymnasium’s env for more information.

      +
      +

      Reward space:

      +

      By default, the reward space is a 3D vector containing the time penalty, and penalties for reversing and going forward.

      +
        +
      • time penalty: -1.0 for each time step

      • +
      • reverse penalty: -1.0 for each time step the action is 0 (reverse)

      • +
      • forward penalty: -1.0 for each time step the action is 2 (forward)

      • +
      +

      #Alternatively, the reward can be changed with the following options:

      +
        +
      • add_speed_objective: Add an extra objective corresponding to the speed of the car.

      • +
      • remove_move_penalty: Remove the reverse and forward objectives.

      • +
      • merge_move_penalty: Merge reverse and forward penalties into a single penalty.

      • +
      +
      +
      + +
      +
      +
      + + +
      +
      + + +
      +
      + +
      +
      + +
      +
      + +
      +
      +
      + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/environments/mo-mountaincar-timemove/index.html b/environments/mo-mountaincar-timemove/index.html new file mode 100644 index 00000000..8e9b9c9d --- /dev/null +++ b/environments/mo-mountaincar-timemove/index.html @@ -0,0 +1,662 @@ + + + + + + + + + + + + + + + MO-Mountaincar-Timemove - MO-Gymnasium Documentation + + + + + + + + + +
      +
      +
      + +
      + +
      +
      + + +
      +
      +
      +
      + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + +
      + + +
      + + + + + +
      +
      +
      + + + + + Back to top + +
      + +
      + +
      +
      + +
      +

      MO-Mountaincar-Timemove

      +
      +_static/videos/mo-mountaincar-timemove.gif + +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      Action Space

      Discrete(3)

      Observation Shape

      (2,)

      Observation High

      [0.6 0.07]

      Observation Low

      [-1.2 -0.07]

      Reward Shape

      (2,)

      Reward High

      [-1. 0.]

      Reward Low

      [-1. -1.]

      Import

      mo_gymnasium.make("mo-mountaincar-timemove-v0")

      +
      +

      A multi-objective version of the MountainCar environment, where the goal is to reach the top of the mountain.

      +

      See Gymnasium’s env for more information.

      +
      +

      Reward space:

      +

      By default, the reward space is a 3D vector containing the time penalty, and penalties for reversing and going forward.

      +
        +
      • time penalty: -1.0 for each time step

      • +
      • reverse penalty: -1.0 for each time step the action is 0 (reverse)

      • +
      • forward penalty: -1.0 for each time step the action is 2 (forward)

      • +
      +

      #Alternatively, the reward can be changed with the following options:

      +
        +
      • add_speed_objective: Add an extra objective corresponding to the speed of the car.

      • +
      • remove_move_penalty: Remove the reverse and forward objectives.

      • +
      • merge_move_penalty: Merge reverse and forward penalties into a single penalty.

      • +
      +
      +
      + +
      +
      +
      + + +
      +
      + + +
      +
      + +
      +
      + +
      +
      + +
      +
      +
      + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/environments/mo-mountaincar-timespeed/index.html b/environments/mo-mountaincar-timespeed/index.html new file mode 100644 index 00000000..cb088892 --- /dev/null +++ b/environments/mo-mountaincar-timespeed/index.html @@ -0,0 +1,662 @@ + + + + + + + + + + + + + + + MO-Mountaincar-Timespeed - MO-Gymnasium Documentation + + + + + + + + + +
      +
      +
      + +
      + +
      +
      + + +
      +
      +
      +
      + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + +
      + + +
      + + + + + +
      +
      +
      + + + + + Back to top + +
      + +
      + +
      +
      + +
      +

      MO-Mountaincar-Timespeed

      +
      +_static/videos/mo-mountaincar-timespeed.gif + +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      Action Space

      Discrete(3)

      Observation Shape

      (2,)

      Observation High

      [0.6 0.07]

      Observation Low

      [-1.2 -0.07]

      Reward Shape

      (2,)

      Reward High

      [-1. 1.1]

      Reward Low

      [-1. 0.]

      Import

      mo_gymnasium.make("mo-mountaincar-timespeed-v0")

      +
      +

      A multi-objective version of the MountainCar environment, where the goal is to reach the top of the mountain.

      +

      See Gymnasium’s env for more information.

      +
      +

      Reward space:

      +

      By default, the reward space is a 3D vector containing the time penalty, and penalties for reversing and going forward.

      +
        +
      • time penalty: -1.0 for each time step

      • +
      • reverse penalty: -1.0 for each time step the action is 0 (reverse)

      • +
      • forward penalty: -1.0 for each time step the action is 2 (forward)

      • +
      +

      #Alternatively, the reward can be changed with the following options:

      +
        +
      • add_speed_objective: Add an extra objective corresponding to the speed of the car.

      • +
      • remove_move_penalty: Remove the reverse and forward objectives.

      • +
      • merge_move_penalty: Merge reverse and forward penalties into a single penalty.

      • +
      +
      +
      + +
      +
      +
      + + +
      +
      + + +
      +
      + +
      +
      + +
      +
      + +
      +
      +
      + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/environments/mo-mountaincar/index.html b/environments/mo-mountaincar/index.html index ed96e5c4..21fefc46 100644 --- a/environments/mo-mountaincar/index.html +++ b/environments/mo-mountaincar/index.html @@ -11,7 +11,7 @@ - + MO-Mountaincar - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
    • Install
    • API
    • Wrappers
    • +
    • Vector Wrappers
    • MORL Baselines

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Mountaincar#

    +

    MO-Mountaincar

    -../../_images/mo-mountaincar.gif +../../_images/mo-mountaincar.gif +
    @@ -359,13 +364,19 @@

    MO-MountaincarGymnasium’s env for more information.

    -

    Reward space:#

    -

    The reward space is a 3D vector containing the time penalty, and penalties for reversing and going forward.

    +

    Reward space:

    +

    By default, the reward space is a 3D vector containing the time penalty, and penalties for reversing and going forward.

    • time penalty: -1.0 for each time step

    • reverse penalty: -1.0 for each time step the action is 0 (reverse)

    • forward penalty: -1.0 for each time step the action is 2 (forward)

    +

    #Alternatively, the reward can be changed with the following options:

    +
      +
    • add_speed_objective: Add an extra objective corresponding to the speed of the car.

    • +
    • remove_move_penalty: Remove the reverse and forward objectives.

    • +
    • merge_move_penalty: Merge reverse and forward penalties into a single penalty.

    • +
    @@ -462,8 +473,8 @@

    Reward space: - + + diff --git a/environments/mo-mountaincarcontinuous/index.html b/environments/mo-mountaincarcontinuous/index.html index 181ab110..0132d200 100644 --- a/environments/mo-mountaincarcontinuous/index.html +++ b/environments/mo-mountaincarcontinuous/index.html @@ -11,7 +11,7 @@ - + MO-Mountaincarcontinuous - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Mountaincarcontinuous#

    +

    MO-Mountaincarcontinuous

    -../../_images/mo-mountaincarcontinuous.gif +../../_images/mo-mountaincarcontinuous.gif +
  • @@ -359,7 +364,7 @@

    MO-Mountaincarcontinuoussource for more information.

    -

    Reward space:#

    +

    Reward space:

    The reward space is a 2D vector containing the time penalty and the fuel reward.

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Reacher#

    +

    MO-Reacher

    -../../_images/mo-reacher.gif +../../_images/mo-reacher.gif +
  • @@ -357,11 +362,11 @@

    MO-Reacher -

    Description#

    -

    Mujoco version of mo-reacher-v0, based on Reacher-v4 environment.

    +

    Description

    +

    Multi-objective version of the Reacher-v4 environment.

    -

    Observation Space#

    +

    Observation Space

    The observation is 6-dimensional and contains:

    • sin and cos of the angles of the central and elbow joints

    • @@ -369,11 +374,11 @@

      Observation Space -

      Action Space#

      +

      Action Space

      The action space is discrete and contains the 3^2=9 possible actions based on applying positive (+1), negative (-1) or zero (0) torque to each of the two joints.

    -

    Reward Space#

    +

    Reward Space

    The reward is 4-dimensional and is defined based on the distance of the tip of the arm and the four target locations. For each i={1,2,3,4} it is computed as:

    @@ -357,12 +362,12 @@

    MO-Supermario -

    Description#

    +

    Description

    Multi-objective version of the SuperMarioBro environment.

    See gym-super-mario-bros for more information.

    -

    Reward Space#

    +

    Reward Space

    The reward is a 5-dimensional vector:

    • 0: How far Mario moved in the x position

    • @@ -373,7 +378,7 @@

      Reward Space -

      Episode Termination#

      +

      Episode Termination

      The episode terminates when Mario dies or reaches the flag.

    @@ -473,8 +478,8 @@

    Episode Termination - + + diff --git a/environments/mo-swimmer/index.html b/environments/mo-swimmer/index.html index 1debafa9..48809779 100644 --- a/environments/mo-swimmer/index.html +++ b/environments/mo-swimmer/index.html @@ -11,7 +11,7 @@ - + MO-Swimmer - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Swimmer#

    +

    MO-Swimmer

    -../../_images/mo-swimmer.gif +../../_images/mo-swimmer.gif +
  • @@ -357,12 +362,15 @@

    MO-Swimmer -

    Description#

    +

    Description

    Multi-objective version of the SwimmerEnv environment.

    See Gymnasium’s env for more information.

    +

    The original Gymnasium’s ‘Swimmer-v4’ is recovered by the following linear scalarization:

    +

    env = mo_gym.make(‘mo-swimmer-v4’) +LinearReward(env, weight=np.array([1.0, 1e-4]))

    -

    Reward Space#

    +

    Reward Space

    The reward is 2-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    MO-Walker2D#

    +

    MO-Walker2D

    -../../_images/mo-walker2d.gif +../../_images/mo-walker2d.gif +
  • @@ -357,12 +362,12 @@

    MO-Walker2D -

    Description#

    +

    Description

    Multi-objective version of the Walker2dEnv environment.

    See Gymnasium’s env for more information.

    -

    Reward Space#

    +

    Reward Space

    The reward is 2-dimensional:

    Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,7 +330,7 @@
    -

    MuJoCo#

    +

    MuJoCo

    Multi-objective versions of Mujoco environments.

  • @@ -466,8 +470,8 @@

    MuJoCo# - + + diff --git a/environments/resource-gathering/index.html b/environments/resource-gathering/index.html index ec85bd29..11289649 100644 --- a/environments/resource-gathering/index.html +++ b/environments/resource-gathering/index.html @@ -11,7 +11,7 @@ - + Resource-Gathering - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Resource-Gathering#

    +

    Resource-Gathering

    -../../_images/resource-gathering.gif +../../_images/resource-gathering.gif +
  • @@ -357,12 +362,12 @@

    Resource-Gathering -

    Description#

    +

    Description

    From “Barrett, Leon & Narayanan, Srini. (2008). Learning all optimal policies with multiple criteria. Proceedings of the 25th International Conference on Machine Learning. 41-47. 10.1145/1390156.1390162.”

    -

    Observation Space#

    +

    Observation Space

    The observation is discrete and consists of 4 elements:

    • 0: The x coordinate of the agent

    • @@ -372,7 +377,7 @@

      Observation Space -

      Action Space#

      +

      Action Space

      The action is discrete and consists of 4 elements:

      • 0: Move up

      • @@ -382,7 +387,7 @@

        Action Space -

        Reward Space#

        +

        Reward Space

        The reward is 3-dimensional:

        • 0: -1 if killed by an enemy, else 0

        • @@ -391,15 +396,15 @@

          Reward Space -

          Starting State#

          +

          Starting State

          The agent starts at the home position with no gold or diamond.

    -

    Episode Termination#

    +

    Episode Termination

    The episode terminates when the agent returns home, or when the agent is killed by an enemy.

    -

    Credits#

    +

    Credits

    The home asset is from https://limezu.itch.io/serenevillagerevamped The gold, enemy and gem assets are from https://ninjikin.itch.io/treasure

    @@ -504,8 +509,8 @@

    Credits#< - - + + diff --git a/environments/water-reservoir/index.html b/environments/water-reservoir/index.html index a3b7dbb0..1168e99d 100644 --- a/environments/water-reservoir/index.html +++ b/environments/water-reservoir/index.html @@ -11,7 +11,7 @@ - + Water-Reservoir - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -317,9 +321,10 @@
    -

    Water-Reservoir#

    +

    Water-Reservoir

    -../../_images/water-reservoir.gif +../../_images/water-reservoir.gif +
  • @@ -357,7 +362,7 @@

    Water-Reservoir -

    Description#

    +

    Description

    A Water reservoir environment. The agent executes a continuous action, corresponding to the amount of water released by the dam.

    A. Castelletti, F. Pianosi and M. Restelli, “Tree-based Fitted Q-iteration for Multi-Objective Markov Decision problems,” @@ -365,16 +370,16 @@

    Description -

    Observation Space#

    +

    Observation Space

    The observation is a float corresponding to the current level of the reservoir.

    -

    Action Space#

    +

    Action Space

    The action is a float corresponding to the amount of water released by the dam. If normalized_action is True, the action is a float between 0 and 1 corresponding to the percentage of water released by the dam.

    -

    Reward Space#

    +

    Reward Space

    There are up to 4 rewards:

    • cost due to excess level wrt a flooding threshold (upstream)

    • @@ -385,11 +390,11 @@

      Reward Space -

      Starting State#

      +

      Starting State

      The reservoir is initialized with a random level between 0 and 160.

    -

    Arguments#

    +

    Arguments

    - render_mode: The render mode to use. Can be 'human', 'rgb_array' or 'ansi'.
     - time_limit: The maximum number of steps until the episode is truncated.
     - nO: The number of objectives to use. Can be 2, 3 or 4.
    @@ -400,7 +405,7 @@ 

    Arguments -

    Credits#

    +

    Credits

    Code from: Mathieu Reymond. Ported from: @@ -508,8 +513,8 @@

    Credits#< - - + + diff --git a/examples/citation/index.html b/examples/citation/index.html index baae2629..89d6c0c3 100644 --- a/examples/citation/index.html +++ b/examples/citation/index.html @@ -11,7 +11,7 @@ - + Citation - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,7 +330,7 @@
    -

    Citation#

    +

    Citation

    If you use this repository in your research, please cite:

  • Miscellaneous @@ -326,7 +330,7 @@
  • +
    Vector Wrappers
    @@ -416,8 +420,8 @@

    MORL Baselines - + + diff --git a/examples/publications/index.html b/examples/publications/index.html index 679e9a25..04305791 100644 --- a/examples/publications/index.html +++ b/examples/publications/index.html @@ -11,7 +11,7 @@ - + List of Publications - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,7 +330,7 @@
    -

    List of Publications#

    +

    List of Publications

    MO-Gymnasium (formerly MO-Gym) was first published in:

    Environments

    @@ -232,11 +233,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -323,7 +327,7 @@

    Index

    L

  • @@ -333,15 +337,21 @@

    L

    M

    @@ -412,8 +422,8 @@

    M

    - - + + diff --git a/index.html b/index.html index 899ecde3..6b7864c4 100644 --- a/index.html +++ b/index.html @@ -11,7 +11,7 @@ - + MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -344,12 +348,13 @@

    -MO-HalfCheetah gif +MO-HalfCheetah gif +

    MO-Gymnasium is an open source Python library for developing and comparing multi-objective reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Essentially, the environments follow the standard Gymnasium API, but return vectorized rewards as numpy arrays.

    The documentation website is at mo-gymnasium.farama.org, and we have a public discord server (which we also use to coordinate development work) that you can join here: https://discord.gg/bnJ6kubTg6.

    -

    API#

    +

    API

    As for Gymnasium, the MO-Gymnasium API models environments as simple Python env classes. Creating environment instances and interacting with them is very simple - here’s an example using the “minecart-v0” environment:

    import gymnasium as gym
     import mo_gymnasium as mo_gym
    @@ -363,14 +368,14 @@ 

    API#

    next_obs, vector_reward, terminated, truncated, info = env.step(your_agent.act(obs)) # Optionally, you can scalarize the reward function with the LinearReward wrapper -env = mo_gym.LinearReward(env, weight=np.array([0.8, 0.2, 0.2])) +env = mo_gym.wrappers.LinearReward(env, weight=np.array([0.8, 0.2, 0.2]))

    For details on multi-objective MDP’s (MOMDP’s) and other MORL definitions, see A practical guide to multi-objective reinforcement learning and planning.

    You can also check more examples in this colab notebook! MO-Gym Demo in Colab

    -

    Install#

    +

    Install

    To install MO-Gymnasium, use:

    -

    Citing#

    +

    Citing

    If you use this repository in your research, please cite:

    @inproceedings{felten_toolkit_2023,
     	author = {Felten, Florian and Alegre, Lucas N. and Now{\'e}, Ann and Bazzan, Ana L. C. and Talbi, El Ghazali and Danoy, Gr{\'e}goire and Silva, Bruno C. {\relax da}},
    @@ -454,8 +459,8 @@ 

    Citing# - + + diff --git a/introduction/api/index.html b/introduction/api/index.html index 9f36acf2..91ab4264 100644 --- a/introduction/api/index.html +++ b/introduction/api/index.html @@ -11,7 +11,7 @@ - + API - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,7 +330,7 @@
    -

    API#

    +

    API

    As for Gymnasium, the MO-Gymnasium API models environments as simple Python env classes. Creating environment instances and interacting with them is very simple - here’s an example using the “minecart-v0” environment:

    import gymnasium as gym
     import mo_gymnasium as mo_gym
    @@ -340,7 +344,7 @@ 

    API#

    next_obs, vector_reward, terminated, truncated, info = env.step(your_agent.act(obs)) # Optionally, you can scalarize the reward function with the LinearReward wrapper -env = mo_gym.LinearReward(env, weight=np.array([0.8, 0.2, 0.2])) +env = mo_gym.wrappers.LinearReward(env, weight=np.array([0.8, 0.2, 0.2]))

    For details on multi-objective MDP’s (MOMDP’s) and other MORL definitions, see A practical guide to multi-objective reinforcement learning and planning.

    @@ -421,8 +425,8 @@

    API#

    - - + + diff --git a/introduction/install/index.html b/introduction/install/index.html index 549b43b3..d09f6d3b 100644 --- a/introduction/install/index.html +++ b/introduction/install/index.html @@ -11,7 +11,7 @@ - + Install - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,7 +330,7 @@
    -

    Install#

    +

    Install

    To install MO-Gymnasium, use:

    pip install mo-gymnasium
     
    @@ -408,8 +412,8 @@

    Install#< - - + + diff --git a/objects.inv b/objects.inv index 33c144de4eb50845fd61643b251d8492efe0f9fa..3ba6d0782e0893d074bd6c05b4e7acc7a196291f 100644 GIT binary patch delta 1417 zcmV;41$O%B3h@h&dVj5%U2~);6o&8p6;#dMGVXj_O>J#ezIV2gnN;`G1LzS z1(;6nuiqnzA_Cfzy-5(x`y4(LUhR@UZjWU}B&*B!2SN6GD&+fHR#75W^gu+8_Qx+- zL8L?0Dg?}i;}bWAFi$zLYE&`fc0BDfVjp2S}ttL&$~xT;?MC+aElf@7I}ac-7( zFEIMIbZU$Yb!F!9!rt4ZoPR)P^ZB2|Q}T@n3-Zl8w5O%^!xr=+_^6+j-^DD*e9|K@ z--GZ?|M~aNNPnvQOU|=~372W}2VrziwiJEi3SINM%9GXA+2!4p9oyEV=(L1rHT3(^*0VJanfk;DfT zBu-ZdT{cx~sQS@WCCip!d5(VJrn&G%+mtL^q!W_%>*~pR*R_q)J~6pFa2`%#0#bC{ zl^g)C1%DQtmtlg5NSmER;1v`dhNzipMiHLiAp|J83Wo9!%a96rGDrClFLCAORS1Q29lSKEyXld)b( z-6xsRq97IJwex6~9Fr;DyCH49Pd${j%Z1`7+}myeII0zybDm5 zq~a{j>S9_{cQGLqbwvb*`z((S(2mo@TU~LhE27k9g9h7B!+pFofhC#bYBu$*ZIZSG z|9_+igH1m=7As8S?}qCb5`W4UQX$d><4>1SwqhkMdF0>=#82}4=o)6T$vvg{De1Jp zn@_jI5-z(1yaG327G8chfMI)yE`1lGp{uq$!N?k(x{?USW27NU!`A)fly_id8ShpX z(vsTp1alz8GZpdu!^CLNLwn)gclAF!i+_wvH-o>Ld)%aYoTDy%e~Y7kBgR8Ty! zP=0{xl@_sVw=`f~FBC-CZpbS)zGn%caXx;?$lxqO^%f@#96ZcoV(VhO$fc9-_bCI? z_8~!taUz4v@f|^>FQS)sc?EhROrVp8u!fBT5EsXx`SvH*B2?H`2E1KCmZRT^|7TTvIA>(5vB*I{lKDbf8meCtO*(APFp z*fmH|Y!xNn$$yqpyp_gqs-2#bE#;O##Vb=%*nC~ia?5wDeh*Wum|98+M?~hWZ2k{9dy6IaH zK(-1iDVsFGXO#9PQFsH*h@o0$>?+b5Jf;A}wP9h=7VZ?Q@lh(CUd@antk>j<3RaVkouSo*w{`0_Q zE6O<$RJPGqyOEfP%%-_+S1djYvn0hI>N9G{@BsYbYR1?T{!;BJHDsRy1!<1<=lDEF zvfchjPFY-+(xJL`3WHyWHY->zF9hNn2+XO{mVfvx&t-HZ*Gw?ZZ2gcFkQNW1ZHSO8 z&+2nJRR1s|@m3Ip;d55!7Z~O_;++fJxj>d`FKDn0EqspG4iHFYW7$i+@0$(VfPXTC z!KI&FOOWR9_v3R8xo#bcf_RuJy#}Zv@Eys{zfW<{P1l}bbPFGyhA1X;q%q6E9*4&{ z?|;aeCO@q{r6YCi8Rkg%cdGJV0-xhRPu)ZO6=?sJRc2+b8}#j8@-kYBq8e&rMe&Xp z*tp2(o=Gjy#>Z}bY>Gb;C?Rk{nyOdH()$(VmFUR@Ha<|$AEY^0rw69nS^Nnq?gS6l zw?<(D758~dH04l;^rC#4VmEwqhc5i{h<`e?TMkyy@|QtjLW%C6fFk*V9)f5w^)Jf? zdUP@-n_B4~U#kjPu^+48*z{FCD!|Le#mDNxCnBIe&$% zfukrk97$zpIRbfLF4EH8Ud&{pnK-BI>_h+Ifqzuc8^A@5J*E?kXCwQFbZcb zASs^sfMq~j@K>?Il0RbOf}TW$C4Eu8c23LXdqjriISKIvdXz=wIH?Q@av%i6>c7AknZO!zggs^~K5VkLU)`s{1D!n8&zMmUt(RvXzo13#SWQa==9 p_yo9jXasfPa9YKZM_8VJ>=OFP&t5);Gn&7dL{ELI_#aP|qHFK0bOZnZ diff --git a/release_notes/index.html b/release_notes/index.html index baa0c43f..45bf2050 100644 --- a/release_notes/index.html +++ b/release_notes/index.html @@ -11,7 +11,7 @@ - + Release Notes - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,9 +330,54 @@
    -

    Release Notes#

    +

    Release Notes

    +
    +

    v1.2.0: MO-Gymnasium 1.2.0 Release: Update Gymnasium to v1.0.0, New Mountaincar Environments, Documentation and Test Improvements, and more

    +

    Released on 2024-10-16 - GitHub - PyPI

    +

    Breaking Changes

    +
      +
    • Similar to Gymnasium v1.0, VecEnvs now differ from normal Envs. The associated wrappers also differ. See Gymnasium 1.0.0 release notes.
    • +
    • Wrappers have been moved to their wrappers subpackage, e.g., from mo_gymnasium import MORecordEpisodeStatistics -> from mo_gymnasium.wrappers import MORecordEpisodeStatistics. Vector wrappers can be found under mo-gymnasium.wrappers.vector. See the tests/ folder or our documentation for example usage.
    • +
    +

    Environments

    + +

    Documentation and Tests

    + +

    Bug Fixes

    + +

    New Contributors

    + +

    Full Changelog: v1.1.0...v1.2.0

    -

    v1.1.0: MO-Gymnasium 1.1.0 Release: New MuJoCo environments, Mirrored Deep Sea Treasure, Fruit Tree rendering, and more#

    +

    v1.1.0: MO-Gymnasium 1.1.0 Release: New MuJoCo environments, Mirrored Deep Sea Treasure, Fruit Tree rendering, and more

    Released on 2024-03-11 - GitHub - PyPI

    Environments

      @@ -352,7 +401,7 @@

      Bug fixes

    Full Changelog: v1.0.1...v1.1.0

    -

    v1.0.1: MO-Gymnasium 1.0.1 Release: Support Gymnasium 0.29, breakable-bottles pygame render, and more#

    +

    v1.0.1: MO-Gymnasium 1.0.1 Release: Support Gymnasium 0.29, breakable-bottles pygame render, and more

    Released on 2023-08-24 - GitHub - PyPI

    Environments

      @@ -379,7 +428,7 @@

      Bug fixes

    Full Changelog: v1.0.0...v1.0.1

    -

    v1.0.0: MO-Gymnasium becomes mature#

    +

    v1.0.0: MO-Gymnasium becomes mature

    Released on 2023-06-12 - GitHub - PyPI

    MO-Gymnasium 1.0.0 Release Notes

    We are thrilled to introduce the mature release of MO-Gymnasium, a standardized API and collection of environments designed for Multi-Objective Reinforcement Learning (MORL).

    @@ -391,7 +440,7 @@

    MO-Gymnasium 1.0.0 Release Notes

    reward = forward_reward - ctrl_cost

    With MORL, users have the flexibility to determine the compromises they desire based on their preferences for each objective. Consequently, the environments in MO-Gymnasium do not have predefined weights. Thus, MO-Gymnasium extends the capabilities of Gymnasium to the multi-objective setting, where the agents receives a vectorial reward.

    For example, here is an illustration of the multiple policies learned by an MORL agent for the mo-halfcheetah domain, balancing between saving battery and speed:

    - +

    This release marks the first mature version of MO-Gymnasium within Farama, indicating that the API is stable, and we have achieved a high level of quality in this library.

    API

    v0.3.4...v1.0.0

    -

    v0.3.4: MO-Gymnasium 0.3.4 Release: Known Pareto Front, improved renders and documentation#

    +

    v0.3.4: MO-Gymnasium 0.3.4 Release: Known Pareto Front, improved renders and documentation

    Released on 2023-03-14 - GitHub - PyPI

    Changelogs

    Environments

    @@ -470,7 +519,7 @@

    New Contributors

    Full Changelog: v0.3.3...0.3.4

    -

    v0.3.3: MO-Gymnasium 0.3.3 Release: Policy Evaluation bug fix, better documentation page#

    +

    v0.3.3: MO-Gymnasium 0.3.3 Release: Policy Evaluation bug fix, better documentation page

    Released on 2023-02-13 - GitHub - PyPI

    New improvements/features

      @@ -489,7 +538,7 @@

      Documentation

    Full Changelog: 0.3.2...v0.3.3

    -

    MO-Gymnasium 0.3.2 Release: Bug fixes, improved webpage#

    +

    MO-Gymnasium 0.3.2 Release: Bug fixes, improved webpage

    Released on 2023-02-03 - GitHub - PyPI

    Bug fixes

      @@ -502,7 +551,7 @@

      Documentation

    Full Changelog: 0.3.1...0.3.2

    -

    MO-Gymnasium 0.3.1 Release: Improved documentation and MuJoco MO-Reacher environment#

    +

    MO-Gymnasium 0.3.1 Release: Improved documentation and MuJoco MO-Reacher environment

    Released on 2023-02-02 - GitHub - PyPI

    This minor release adds "mo-reacher-v4", a MuJoco version of the Reacher environment, fixes a bug in Lunar Lander and improves the library documentation.

    Environments

    @@ -521,7 +570,7 @@

    Bug Fixes

    Full Changelog: 0.3.0...0.3.1

    -

    MO-Gymnasium 0.3.0 Release: Migrate to Gymnasium#

    +

    MO-Gymnasium 0.3.0 Release: Migrate to Gymnasium

    Released on 2023-01-23 - GitHub - PyPI

    This release marks our first release as part of the Farama Foundation. Benefitting the Farama structure, this library should reach a higher level of quality and more integration with the tools from the RL community.

    Breaking changes

    @@ -544,7 +593,7 @@

    Community

    Full Changelog: 0.2.1...0.3.0

    -

    0.2.1#

    +

    0.2.1

    Released on 2022-12-09 - GitHub - PyPI

    • 5 new environments: fishwood-v0 (ESR), mo-MountainCarContinuous-v0, water-reservoir-v0, mo-highway-v0 and mo-highway-fast-v0;
    • @@ -554,15 +603,15 @@

      0.2.1 -

      0.2.0#

      +

      0.2.0

      Released on 2022-09-25 - GitHub - PyPI

      Support for new Gym>=0.26 API

    -

    0.1.2#

    +

    0.1.2

    Released on 2022-09-25 - GitHub - PyPI

    -

    0.1.1#

    +

    0.1.1

    Released on 2022-08-24 - GitHub - PyPI

    @@ -615,6 +664,7 @@

    0.1.1

  • Miscellaneous @@ -389,8 +393,8 @@ - - + + diff --git a/searchindex.js b/searchindex.js index c35fa381..9ee85872 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["404", "README", "citing/citing", "community/community", "environments/breakable-bottles", "environments/classical", "environments/deep-sea-treasure", "environments/deep-sea-treasure-concave", "environments/deep-sea-treasure-mirrored", "environments/fishwood", "environments/four-room", "environments/fruit-tree", "environments/grid-world", "environments/minecart", "environments/minecart-deterministic", "environments/minecart-rgb", "environments/misc", "environments/mo-ant", "environments/mo-ant-2d", "environments/mo-halfcheetah", "environments/mo-highway", "environments/mo-highway-fast", "environments/mo-hopper", "environments/mo-hopper-2d", "environments/mo-humanoid", "environments/mo-lunar-lander", "environments/mo-lunar-lander-continuous", "environments/mo-mountaincar", "environments/mo-mountaincarcontinuous", "environments/mo-reacher", "environments/mo-supermario", "environments/mo-swimmer", "environments/mo-walker2d", "environments/mujoco", "environments/resource-gathering", "environments/water-reservoir", "examples/citation", "examples/morl_baselines", "examples/publications", "index", "introduction/api", "introduction/install", "release_notes", "tutorials/custom_env", "wrappers/wrappers"], "filenames": ["404.md", "README.md", "citing/citing.md", "community/community.md", "environments/breakable-bottles.md", "environments/classical.md", "environments/deep-sea-treasure.md", "environments/deep-sea-treasure-concave.md", "environments/deep-sea-treasure-mirrored.md", "environments/fishwood.md", "environments/four-room.md", "environments/fruit-tree.md", "environments/grid-world.md", "environments/minecart.md", "environments/minecart-deterministic.md", "environments/minecart-rgb.md", "environments/misc.md", "environments/mo-ant.md", 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  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -327,10 +331,10 @@
    -

    Creating a custom environment#

    +

    Creating a custom environment

    This tutorials goes through the steps of creating a custom environment for MO-Gymnasium. Since MO-Gymnasium is closely tied to Gymnasium, we will refer to its documentation for some parts.

    -

    1. Create a new environment class#

    +

    1. Create a new environment class

    Create an environment class that inherits from gymnasium.Env. The class must implement the following methods:

    • __init__(self, ...) - The constructor of the class. It should initialize the environment and set the self.action_space andself.observation_space attributes as in classical Gymnasium (see Spaces. Moreover, since we are dealing with multiple objective/rewards, you should define a self.reward_space attribute that defines the shape of the vector rewards returned by the environment, as well as self.reward_dim which is an integer defining the size of the reward vector.

    • @@ -341,7 +345,7 @@

      1. Create a new environment class -

      2. Register the environment#

      +

      2. Register the environment

      Register the environment in the registry. This is done by adding the following line to the __init__.py file in your env directory:

      from gymnasium.envs.registration import register
       register(
      @@ -352,11 +356,11 @@ 

      2. Register the environment -

      3. Test the environment#

      +

      3. Test the environment

      If your environment is registered within the MO-Gymnasium repository (step 2), it should be automatically pulled for testing when you run pytest.

    -

    4. Instantiate your environment#

    +

    4. Instantiate your environment

    See our API documentation, but essentially:

    import mo_gymnasium
     env = mo_gymnasium.make('my_env_v0')
    @@ -461,8 +465,8 @@ 

    4. Instantiate your environment - + + diff --git a/wrappers/vector_wrappers/index.html b/wrappers/vector_wrappers/index.html new file mode 100644 index 00000000..d0fee156 --- /dev/null +++ b/wrappers/vector_wrappers/index.html @@ -0,0 +1,713 @@ + + + + + + + + + + + + + + + Vector Wrappers - MO-Gymnasium Documentation + + + + + + + + + +
    + +
    + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + +
    + + +
    + + + + + +
    +
    +
    + + + + + Back to top + +
    + +
    + +
    +
    + +
    +

    Vector Wrappers

    +

    Similar to the normal wrappers, MO-Gymnasium provides a few wrappers that are specifically designed to work with vectorized environments. They are all available directly from the mo_gymnasium.wrappers.vector module.

    +
    +

    MOSyncVectorEnv

    +
    +
    +class mo_gymnasium.wrappers.vector.MOSyncVectorEnv(env_fns: Iterator[callable], copy: bool = True)
    +

    Vectorized environment that serially runs multiple environments.

    +

    Example

    +
    >>> import mo_gymnasium as mo_gym
    +
    +
    +
    >>> envs = mo_gym.wrappers.vector.MOSyncVectorEnv([
    +...     lambda: mo_gym.make("deep-sea-treasure-v0") for _ in range(4)
    +... ])
    +>>> envs
    +MOSyncVectorEnv(num_envs=4)
    +>>> obs, infos = envs.reset()
    +>>> obs
    +array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=int32)
    +>>> _ = envs.action_space.seed(42)
    +>>> actions = envs.action_space.sample()
    +>>> obs, rewards, terminateds, truncateds, infos = envs.step([0, 1, 2, 3])
    +>>> obs
    +array([[0, 0], [1, 0], [0, 0], [0, 3]], dtype=int32)
    +>>> rewards
    +array([[0., -1.], [0.7, -1.], [0., -1.], [0., -1.]], dtype=float32)
    +>>> terminateds
    +array([False,  True, False, False])
    +
    +
    +
    + +
    +
    +

    MORecordEpisodeStatistics

    +
    +
    +class mo_gymnasium.wrappers.vector.MORecordEpisodeStatistics(env: VectorEnv, gamma: float = 1.0, buffer_length: int = 100, stats_key: str = 'episode')
    +

    This wrapper will keep track of cumulative rewards and episode lengths.

    +

    At the end of any episode within the vectorized env, the statistics of the episode +will be added to info using the key episode, and the _episode key +is used to indicate the environment index which has a terminated or truncated episode.

    +
    +

    For a vectorized environments the output will be in the form of (be careful to first wrap the env into vector before applying MORewordStatistics):

    +
    >>> infos = { 
    +...     "episode": {
    +...         "r": "<array of cumulative reward for each done sub-environment (2d array, shape (num_envs, dim_reward))>",
    +...         "dr": "<array of discounted reward for each done sub-environment (2d array, shape (num_envs, dim_reward))>",
    +...         "l": "<array of episode length for each done sub-environment (array)>",
    +...         "t": "<array of elapsed time since beginning of episode for each done sub-environment (array)>"
    +...     },
    +...     "_episode": "<boolean array of length num-envs>"
    +... }
    +
    +
    +
    +

    Moreover, the most recent rewards and episode lengths are stored in buffers that can be accessed via +wrapped_env.return_queue and wrapped_env.length_queue respectively.

    +
    +
    Variables:
    +
      +
    • return_queue – The cumulative rewards of the last deque_size-many episodes

    • +
    • length_queue – The lengths of the last deque_size-many episodes

    • +
    +
    +
    +
    + +
    +
    + +
    +
    + +
    + +
    +
    +
    + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/wrappers/wrappers/index.html b/wrappers/wrappers/index.html index a73dbd8c..22a8655f 100644 --- a/wrappers/wrappers/index.html +++ b/wrappers/wrappers/index.html @@ -8,10 +8,10 @@ - + - + Wrappers - MO-Gymnasium Documentation @@ -215,6 +215,7 @@
  • Install
  • API
  • Wrappers
  • +
  • Vector Wrappers
  • MORL Baselines
  • Environments

    @@ -234,11 +235,14 @@
  • MO-Mountaincar
  • MO-Mountaincarcontinuous
  • MO-Lunar-Lander
  • +
  • MO-Lunar-Lander-Continuous
  • Miscellaneous @@ -326,77 +330,73 @@
    -

    Wrappers#

    -

    A few wrappers inspired from Gymnasium’s wrappers are available in MO-Gymnasium. They are all available directly from the mo_gymnasium module.

    +

    Wrappers

    +

    A few wrappers inspired from Gymnasium’s wrappers are available in MO-Gymnasium. They are all available directly from the mo_gymnasium.wrappers module.

    -

    LinearReward#

    +

    LinearReward

    -
    -class mo_gymnasium.LinearReward(env: Env, weight: ndarray | None = None)#
    +
    +class mo_gymnasium.wrappers.LinearReward(env: Env, weight: ndarray | None = None)

    Makes the env return a scalar reward, which is the dot-product between the reward vector and the weight vector.

    -

    MONormalizeReward#

    +

    MONormalizeReward

    -
    -class mo_gymnasium.MONormalizeReward(env: Env, idx: int, gamma: float = 0.99, epsilon: float = 1e-08)#
    +
    +class mo_gymnasium.wrappers.MONormalizeReward(env: Env, idx: int, gamma: float = 0.99, epsilon: float = 1e-08)

    Wrapper to normalize the reward component at index idx. Does not touch other reward components.

    +

    This code is heavily inspired on Gymnasium’s except that it extracts the reward component at given idx, normalizes it, and reinjects it.

    +

    (!) This smoothes the moving average of the reward, which can be useful for training stability. But it does not “normalize” the reward in the sense of making it have a mean of 0 and a standard deviation of 1.

    +

    Example

    +
    >>> import mo_gymnasium as mo_gym
    +>>> from mo_gymnasium.wrappers import MONormalizeReward
    +>>> env = mo_gym.make("deep-sea-treasure-v0")
    +>>> norm_treasure_env = MONormalizeReward(env, idx=0)
    +>>> both_norm_env = MONormalizeReward(norm_treasure_env, idx=1)
    +>>> both_norm_env.reset() # This one normalizes both rewards
    +
    +
    -

    MOClipReward#

    +

    MOClipReward

    -
    -class mo_gymnasium.MOClipReward(env: Env, idx: int, min_r, max_r)#
    +
    +class mo_gymnasium.wrappers.MOClipReward(env: Env, idx: int, min_r, max_r)

    Clip reward[idx] to [min, max].

    -
    -
    -

    MOSyncVectorEnv#

    -
    -
    -class mo_gymnasium.MOSyncVectorEnv(env_fns: Iterator[callable], copy: bool = True)#
    -

    Vectorized environment that serially runs multiple environments.

    -
    -
    -

    MORecordEpisodeStatistics#

    +

    MORecordEpisodeStatistics

    -
    -class mo_gymnasium.MORecordEpisodeStatistics(env: Env, gamma: float = 1.0, deque_size: int = 100)#
    +
    +class mo_gymnasium.wrappers.MORecordEpisodeStatistics(env: Env, gamma: float = 1.0, buffer_length: int = 100, stats_key: str = 'episode')

    This wrapper will keep track of cumulative rewards and episode lengths.

    After the completion of an episode, info will look like this:

    >>> info = {
     ...     "episode": {
     ...         "r": "<cumulative reward (array)>",
     ...         "dr": "<discounted reward (array)>",
    -...         "l": "<episode length (scalar)>", # contrary to Gymnasium, these are not a numpy array
    +...         "l": "<episode length (scalar)>",
     ...         "t": "<elapsed time since beginning of episode (scalar)>"
     ...     },
     ... }
     
    -

    For a vectorized environments the output will be in the form of (be careful to first wrap the env into vector before applying MORewordStatistics):

    -
    >>> infos = {
    -...     "final_observation": "<array of length num-envs>",
    -...     "_final_observation": "<boolean array of length num-envs>",
    -...     "final_info": "<array of length num-envs>",
    -...     "_final_info": "<boolean array of length num-envs>",
    -...     "episode": {
    -...         "r": "<array of cumulative reward (2d array, shape (num_envs, dim_reward))>",
    -...         "dr": "<array of discounted reward (2d array, shape (num_envs, dim_reward))>",
    -...         "l": "<array of episode length (array)>",
    -...         "t": "<array of elapsed time since beginning of episode (array)>"
    -...     },
    -...     "_episode": "<boolean array of length num-envs>"
    -... }
    -
    -
    +
    + +
    +
    +

    MOMaxAndSkipObservation

    +
    +
    +class mo_gymnasium.wrappers.MOMaxAndSkipObservation(env: Env[ObsType, ActType], skip: int = 4)
    +

    This wrapper will return only every skip-th frame (frameskipping) and return the max between the two last observations.

    +

    Note: This wrapper is based on the wrapper from stable-baselines3: https://stable-baselines3.readthedocs.io/en/master/_modules/stable_baselines3/common/atari_wrappers.html#MaxAndSkipEnv

    @@ -407,12 +407,12 @@

    MORecordEpisode