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An index of algorithms for offline reinforcement learning (offline-rl)
| Date | Stars |
|---|---|
| 2026-07-24 | 1073 |
| 2026-07-25 | 1073 |
| 2026-07-28 | 1073 |
| 2026-07-30 | 1073 |
| 2026-07-31 | 1073 |
| 2026-08-04 | 1074 |
| 2026-08-05 | 1075 |
| 2026-08-06 | 1075 |
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# awesome-offline-rl This is a collection of research and review papers for **offline reinforcement learning (offline rl)**. Feel free to star and fork. Maintainers: - [Haruka Kiyohara](https://sites.google.com/view/harukakiyohara) (Cornell University) - [Yuta Saito](https://usait0.com/en/) (Hanjuku-kaso Co., Ltd. / Cornell University) We are looking for more contributors and maintainers! Please feel free to [pull requests](https://github.com/usaito/awesome-offline-rl/pulls). ``` format: - [title](paper link) [links] - author1, author2, and author3. arXiv/conferences/journals/, year. ``` For any questions, feel free to contact: [email protected] ## Table of Contents - [Papers](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#papers) - [Review/Survey/Position Papers](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#reviewsurveyposition-papers) - [Offline RL](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#offline-rl) - [Off-Policy Evaluation and Learning](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#off-policy-evaluation-and-learning) - [Related Reviews](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#related-reviews) - [Offline RL: Theory/Methods](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#offline-rl-theorymethods) - [Offline RL: Benchmarks/Experiments](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#offline-rl-benchmarksexperiments) - [Offline RL: Applications](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#offline-rl-applications) - [Off-Policy Evaluation and Learning: Theory/Methods](https://github.com/hanjuku-kaso/awesome-offline-rl#off-policy-evaluation-and-learning-theorymethods) - [Off-Policy Evaluation: Contextual Bandits](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#off-policy-evaluation-contextual-bandits) - [Off-Policy Evaluation: Reinforcement Learning](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#off-policy-evaluation-reinforcement-learning) - [Off-Policy Learning](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#off-policy-learning) - [Off-Policy Evaluation and Learning: Benchmarks/Experiments](https://github.com/hanjuku-kaso/awesome-offline-rl#off-policy-evaluation-and-learning-benchmarksexperiments) - [Off-Policy Evaluation and Learning: Applications](https://github.com/hanjuku-kaso/awesome-offline-rl#off-policy-evaluation-and-learning-applications) - [Open Source Software/Implementations](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#open-source-softwareimplementations) - [Blog/Podcast](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#blogpodcast) - [Blog](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#blog) - [Podcast](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#podcast) - [Related Workshops](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#related-workshops) - [Tutorials/Talks/Lectures](https://github.com/hanjuku-kaso/awesome-offline-rl/tree/main#tutorialstalkslectures) ## Papers ### Review/Survey/Position Papers #### Offline RL - [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2307.15217) - Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Segerie, Micah Carroll, Andi Peng, Phillip Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Bıyık, Anca Dragan, David Krueger, Dorsa Sadigh, and Dylan Hadfield-Menell. arXiv, 2023. - [A Survey on Offline Model-Based Reinforcement Learning](https://arxiv.org/abs/2305.03360) - Haoyang He. arXiv, 2023. - [Foundation Models for Decision Making: Problems
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f8491bac532cc9c8, topic:awesome, topic:awesome-list
matched fp:f8491bac532cc9c8, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning