@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md

LAMDA-RL Lab

LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.

We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:

  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.

Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned Loading

  1. OfflineRL-LibOfflineRL-LibPublic

    Benchmarked implementations of Offline RL Algorithms.

    Python 77 7

  2. ODISODISPublic

    The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python 45 6

  3. PRDCPRDCPublic

    Forked from kimoyami/PRDC

    Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python 17 2

  4. ACTACTPublic

    Official code for ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning (AAAI'24)

    Python 16 3

  5. Pretrained_BWArea_2.7B_30GPretrained_BWArea_2.7B_30GPublic

    Pre-trained Models of BWArea Model

    Python 9

  6. CPRCPRPublic

    Forked from LyndonKong/CPR

    Python 4

Repositories

Showing 10 of 49 repositories
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md
LAMDA-RL Lab
LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.
We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:
  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.
Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned
Loading
  1. OfflineRL-LibOfflineRL-LibPublic
    
Benchmarked implementations of Offline RL Algorithms.

    Python
77

7

  2. ODISODISPublic
    
The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python
45

6

  3. PRDCPRDCPublic
    
Forked from kimoyami/PRDC
    
Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python
17

2

  4. ACTACTPublic
    
Official code for ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning (AAAI'24)

    Python
16

3

  5. Pretrained_BWArea_2.7B_30GPretrained_BWArea_2.7B_30GPublic
    
Pre-trained Models of BWArea Model

    Python
9

  6. CPRCPRPublic
    
Forked from LyndonKong/CPR
    Python
4


Repositories

Showing 10 of 49 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md
LAMDA-RL Lab
LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.
We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:
  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.
Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned
Loading
  1. OfflineRL-LibOfflineRL-LibPublic
    
Benchmarked implementations of Offline RL Algorithms.

    Python
77

7

  2. ODISODISPublic
    
The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python
45

6

  3. PRDCPRDCPublic
    
Forked from kimoyami/PRDC
    
Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python
17

2

  4. ACTACTPublic
    
Official code for ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning (AAAI'24)

    Python
16

3

  5. Pretrained_BWArea_2.7B_30GPretrained_BWArea_2.7B_30GPublic
    
Pre-trained Models of BWArea Model

    Python
9

  6. CPRCPRPublic
    
Forked from LyndonKong/CPR
    Python
4


Repositories

Showing 10 of 49 repositories
View all repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md
LAMDA-RL Lab
LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.
We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:
  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.
Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned
Loading
  1. OfflineRL-LibOfflineRL-LibPublic
    
Benchmarked implementations of Offline RL Algorithms.

    Python
77

7

  2. ODISODISPublic
    
The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python
45

6

  3. PRDCPRDCPublic
    
Forked from kimoyami/PRDC
    
Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python
17

2

  4. ACTACTPublic
    
Official code for ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning (AAAI'24)

    Python
16

3

  5. Pretrained_BWArea_2.7B_30GPretrained_BWArea_2.7B_30GPublic
    
Pre-trained Models of BWArea Model

    Python
9

  6. CPRCPRPublic
    
Forked from LyndonKong/CPR
    Python
4


Repositories

Showing 10 of 49 repositories
View all repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md
LAMDA-RL Lab
LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.
We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:
  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.
Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned
Loading
  1. OfflineRL-LibOfflineRL-LibPublic
    
Benchmarked implementations of Offline RL Algorithms.

    Python
77

7

  2. ODISODISPublic
    
The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python
45

6

  3. PRDCPRDCPublic
    
Forked from kimoyami/PRDC
    
Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python
17

2

  4. ACTACTPublic
    
Official code for ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning (AAAI'24)

    Python
16

3

  5. Pretrained_BWArea_2.7B_30GPretrained_BWArea_2.7B_30GPublic
    
Pre-trained Models of BWArea Model

    Python
9

  6. CPRCPRPublic
    
Forked from LyndonKong/CPR
    Python
4


Repositories

Showing 10 of 49 repositories
View all repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md
LAMDA-RL Lab
LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.
We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:
  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.
Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned
Loading
  1. OfflineRL-LibOfflineRL-LibPublic
    
Benchmarked implementations of Offline RL Algorithms.

    Python
77

7

  2. ODISODISPublic
    
The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python
45

6

  3. PRDCPRDCPublic
    
Forked from kimoyami/PRDC
    
Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python
17

2

  4. ACTACTPublic
    
Official code for ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning (AAAI'24)

    Python
16

3

  5. Pretrained_BWArea_2.7B_30GPretrained_BWArea_2.7B_30GPublic
    
Pre-trained Models of BWArea Model

    Python
9

  6. CPRCPRPublic
    
Forked from LyndonKong/CPR
    Python
4


Repositories

Showing 10 of 49 repositories
View all repositories
Top languages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md
LAMDA-RL Lab
LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.
We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:
  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.
Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned
Loading
  1. OfflineRL-LibOfflineRL-LibPublic
    
Benchmarked implementations of Offline RL Algorithms.

    Python
77

7

  2. ODISODISPublic
    
The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python
45

6

  3. PRDCPRDCPublic
    
Forked from kimoyami/PRDC
    
Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python
17

2

  4. ACTACTPublic
    
Official code for ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning (AAAI'24)

    Python
16

3

  5. Pretrained_BWArea_2.7B_30GPretrained_BWArea_2.7B_30GPublic
    
Pre-trained Models of BWArea Model

    Python
9

  6. CPRCPRPublic
    
Forked from LyndonKong/CPR
    Python
4


Repositories

Showing 10 of 49 repositories
View all repositories
Top languages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
@LAMDA-RL

LAMDA-RL

We are a fork of reinforcement learning researchers from LAMDA Group @ Nanjing University.
README.md
LAMDA-RL Lab
LAMDA-RL Lab is at the forefront of advancing the field of reinforcement learning and its application to creating general decision-making intelligence, by pushing the boundaries of what's possible with RL techniques.
We focus on developing novel algorithms and architectures that enable RL systems to learn and make decisions in increasingly general and adaptable ways. Some key areas we are exploring include:
  • Imitation learning;
  • Offline reinforcement learning;
  • Model-based RL and world model learning;
  • Multi-agent and collaborative RL;
  • Planning and learning with large models.
Through both fundamental and application research, our aim is to create RL-based systems that exhibit truly intelligent and general decision-making capabilities. For more information about our lab and research, please refer to our website https://lamda-rl.nju.edu.cn/.

Pinned
Loading
  1. OfflineRL-LibOfflineRL-LibPublic
    
Benchmarked implementations of Offline RL Algorithms.

    Python
77

7

  2. ODISODISPublic
    
The implementation of ICLR 2023 paper "Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data".

    Python
45

6

  3. PRDCPRDCPublic
    
Forked from kimoyami/PRDC
    
Author's PyTorch implementation of ICML'23 paper "Policy Regularization with Dataset Constraint for Offline Reinforcement Learning" for D4RL gym and AntMaze tasks.

    Python
17

2

  4. ACTACTPublic
    
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