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<h1>Resources</h1>
<div class="muted" style="margin-top:-6px;">Reference lists, books, and study roadmaps for research and learning, maintained by SAIL Lab.</div>
<h2>Books</h2>
<h3>Technique Books</h3>
<ul>
<li><p><span style="font-size: 19px;"><a href="http://incompleteideas.net/book/RLbook2020.pdf" target="_blank">Reinforcement Learning: An Introduction</a> by Richard S. Sutton and Andrew G. Barto</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://cdn.jsdelivr.net/gh/it-ebooks-0/it-ebooks-2018-04to07/Algorithms%20for%20Reinforcement%20Learning.pdf" target="_blank">Algorithms for Reinforcement Learning</a> by Csaba Szepesvári</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://rltheorybook.github.io/rltheorybook_ABJKS.pdf" target="_blank">Reinforcement Learning: Theory and Algorithms</a> by Alekh Agarwal, Kianté Brantley, Nan Jiang, Sham M. Kakade, and Wen Sun</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://cs.nyu.edu/~mohri/mlbook/" target="_blank">Foundations of Machine Learning</a> by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.pdf" target="_blank">High-Dimensional Probability: An Introduction with Applications in Data Science</a> by Roman Vershynin</span></p></li>
</ul>
<h3>Philosophy Book</h3>
<ul>
<li><p><span style="font-size: 19px;"><a href="https://archive.org/details/moraldiscoursese00epicuoft/page/n7/mode/2up?view=theater" target="_blank">Moral Discourses: Enchiridion and Fragments</a></span></p></li>
</ul>
<h2>Classical RL Study Roadmap</h2>
<h3>Empirical RL Courses</h3>
<ul>
<li><p><span style="font-size: 19px;"><a href="https://www.davidsilver.uk/teaching/" target="_blank">Reinforcement Learning: An Introduction</a> by David Silver</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://rail.eecs.berkeley.edu/deeprlcourse/" target="_blank">CS285 Deep Reinforcement Learning</a> by Sergey Levine</span></p></li>
</ul>
<h3>Theoretical RL Courses</h3>
<ul>
<li><p><span style="font-size: 19px;"><a href="https://sites.google.com/view/cjin/teaching/ele524-2020-ver" target="_blank">ELE524: Foundations of Reinforcement Learning</a> by Chi Jin</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://sites.google.com/view/cjin/teaching/cos511ece434cos434" target="_blank">COS511/ECE434/COS434: Theoretical Machine Learning</a> by Chi Jin</span></p></li>
<li><p><span style="font-size: 19px;"><a href="http://nanjiang.cs.illinois.edu/cs542/" target="_blank">CS 542 Statistical Reinforcement Learning</a> by Nan Jiang</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://shamulent.github.io/CS_Stat184_Fall22.html" target="_blank">CS/Stat 184: Introduction to Reinforcement Learning</a> by Sham Kakade</span></p></li>
</ul>
<h2>Lab Links</h2>
<ul>
<li><p><span style="font-size: 19px;"><a href="https://github.com/SAIL-Research-Lab" target="_blank">GitHub</a> — open-source code, benchmarks, and datasets.</span></p></li>
<li><p><span style="font-size: 19px;"><a href="https://www.youtube.com/@ai-agent-research" target="_blank">YouTube</a> — recorded talks and seminars.</span></p></li>
</ul>
<h2>Venue Reference</h2>
<ul>
<li><p><span style="font-size: 19px;"><b>CCF Recommended International Conferences and Journals</b> (7th edition, official) — the China Computer Federation’s official ranked list of international conferences and journals (A / B / C classes), widely used as a venue-selection reference. [<a href="./resources/%E7%AC%AC%E4%B8%83%E7%89%88%E4%B8%AD%E5%9B%BD%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%AD%A6%E4%BC%9A%E6%8E%A8%E8%8D%90%E5%9B%BD%E9%99%85%E5%AD%A6%E6%9C%AF%E4%BC%9A%E8%AE%AE%E5%92%8C%E6%9C%9F%E5%88%8A%E7%9B%AE%E5%BD%95%EF%BC%88%E6%AD%A3%E5%BC%8F%E7%89%88%EF%BC%89.pdf" target="_blank">PDF, 72 pages, 中文</a>]</span></p></li>
</ul>
<!-- ========== Temporarily hidden sections: uncomment to restore ==========
<h2>Courses</h2>
<ul>
<li><p><span style="font-size: 19px;"><b>Safe Reinforcement Learning</b> (graduate course, SJTU)</span></p>
<ul>
<li><p><span style="font-size: 17px;"><a href="./courses/safe_rl/Course__Safe_RL_lecture_01.pdf" target="_blank">Lecture 01: Safe Reinforcement Learning — A Brief Introduction</a> (slides, PDF)</span></p></li>
<li><p><span style="font-size: 17px;">Lecture 02: Constrained Markov Decision Processes (slides in preparation)</span></p></li>
</ul>
</li>
</ul>
<h2>Tutorials & Slides</h2>
<ul>
<li><p><span style="font-size: 19px;"><b>Safe Reinforcement Learning: Bridging Theory and Practice</b> — tutorial at <a href="https://ijcai24.org/" target="_blank">IJCAI 2024</a>. [<a href="./tutorials/Safe_RL_IJCAI_Tutorial_2024.pdf" target="_blank">Slides</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>Safe Reinforcement Learning for Smart Grid Control and Operations</b> — tutorial at <a href="https://sgc2024.ieee-smartgridcomm.org/" target="_blank">IEEE SmartGridComm 2024</a>. [<a href="./tutorials/Safe_RL_2024_SmartGridComm.pdf" target="_blank">Slides</a>]</span></p></li>
</ul>
<p class="muted">See <a href="tutorial_talk.html">Tutorials & Talks</a> for the full list of lectures and invited talks.</p>
<h2>Code, Benchmarks & Datasets</h2>
<ul>
<li><p><span style="font-size: 19px;"><b>Robust Gymnasium</b> — a unified modular benchmark for robust reinforcement learning (robustness to uncertainty on state, action, reward, and dynamics). [<a href="https://robust-rl.com/" target="_blank">Website / Code</a>], [<a href="https://github.com/SafeRL-Lab/Robust-Gymnasium" target="_blank">GitHub</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>M4R</b> — a benchmark for massive multimodal understanding and reasoning in open space. [<a href="https://open-space-reasoning.github.io" target="_blank">Dataset, Code & Leaderboard</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>RLBenchNet</b> — a systematic benchmarking suite for evaluating neural network architectures in reinforcement learning. [<a href="https://github.com/SafeRL-Lab/BenchNetRL" target="_blank">GitHub</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>Agentic Web</b> — a study of web agents, with code and evaluation harness. [<a href="https://arxiv.org/pdf/2507.21206" target="_blank">Paper</a>], [<a href="https://github.com/SafeRL-Lab/agentic-web" target="_blank">GitHub</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>CheetahClaws</b> — an open-source agent harness infrastructure for long-horizon, multi-model, and tool-using AI systems. [<a href="https://github.com/SAIL-Research-Lab/cheetahclaws" target="_blank">GitHub</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>AgenticPay</b> — agentic payment infrastructure. [<a href="https://github.com/SAIL-Research-Lab/AgenticPay" target="_blank">GitHub</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>Safe RL Baselines</b> — reference implementations of safe reinforcement learning algorithms. [<a href="https://github.com/chauncygu/Safe-Reinforcement-Learning-Baselines" target="_blank">GitHub</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>Safety benchmarks for robot learning</b> — constrained control suites for safe and safe multi-objective robot learning: [<a href="https://github.com/SafeRL-Lab/Safety-MuJoCo" target="_blank">Safety MuJoCo</a>], [<a href="https://github.com/SafeRL-Lab/Safe-Multi-Objective-MuJoCo" target="_blank">Safe MO MuJoCo</a>], [<a href="https://github.com/chauncygu/Safe-Multi-Agent-Isaac-Gym" target="_blank">Safe Multi-Robot Isaac Gym</a>], [<a href="https://github.com/chauncygu/Safe-Multi-Agent-Mujoco" target="_blank">Safe Multi-Robot MuJoCo</a>], [<a href="https://github.com/chauncygu/Safe-Multi-Agent-Robosuite" target="_blank">Safe Multi-Robot Robosuite</a>], [<a href="https://github.com/chauncygu/Multi-Agent-Constrained-Policy-Optimisation" target="_blank">Safe Multi-Robot Control</a>], [<a href="https://github.com/SafeRL-Lab/Safe-MARL-in-Autonomous-Driving" target="_blank">Safe Multi-Vehicle Driving</a>].</span></p></li>
</ul>
<p class="muted">Browse the <a href="softwares.html">Products & Software</a> page for products and platforms, and the <a href="https://github.com/SAIL-Research-Lab" target="_blank">SAIL Lab GitHub organization</a> for everything else.</p>
<h2>Calls for Papers</h2>
<ul>
<li><p><span style="font-size: 19px;"><b>IEEE TASE Special Issue</b> on <em>Trustworthy AI for Automation Control of Embodied Agents in the Era of Foundation Models</em> (lead guest editor). [<a href="./papers/IEE_TASE_Special_Issue_EAI_CFP.pdf" target="_blank">Call for Papers (PDF)</a>], [<a href="https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8856" target="_blank">Journal</a>]</span></p></li>
</ul>
<h2>Seminar Series</h2>
<ul>
<li><p><span style="font-size: 19px;"><b>Agentic AI Frontier Seminar</b> — an online seminar series on frontier research in agentic AI (speaker lineup, schedule, and past talks). [<a href="https://agentic-ai-frontier-seminar.github.io/" target="_blank">Seminar Homepage</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>Safe RL Seminar</b> — a long-term seminar on safe reinforcement learning, co-launched in December 2022. [<a href="https://sites.google.com/view/saferl-seminar/home" target="_blank">Seminar Homepage</a>]</span></p></li>
<li><p><span style="font-size: 19px;"><b>AIR: International Workshop on AI Agent Reasoning and Decision-Making</b> (1st edition, 2025). [<a href="https://ai-agent-reasoning.com/" target="_blank">Workshop Homepage</a>]</span></p></li>
</ul>
<h2>Getting Started</h2>
<p>
New to safe reinforcement learning? A practical starting point is the
<a href="./courses/safe_rl/Course__Safe_RL_lecture_01.pdf" target="_blank">Lecture 01 slides</a>,
the <a href="./tutorials/Safe_RL_IJCAI_Tutorial_2024.pdf" target="_blank">IJCAI 2024 tutorial</a>, and then
<a href="https://github.com/chauncygu/Safe-Reinforcement-Learning-Baselines" target="_blank">Safe RL Baselines</a> or
<a href="https://robust-rl.com/" target="_blank">Robust Gymnasium</a> to reproduce results.
For LLM agents, start from the <a href="https://cheetahclaws.github.io/" target="_blank">CheetahClaws</a> harness.
</p>
<p class="muted">If you use our code, benchmarks, or course materials, please cite the corresponding paper. Found a broken link? <a href="mailto:sail.lab.hr@gmail.com">Let us know</a>.</p>
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