This is a collection of research and review papers for offline reinforcement learning (offline rl). Feel free to star and fork.

Maintainers:

• Haruka Kiyohara (Tokyo Institute of Technology / Hanjuku-kaso Co., Ltd.)
• Yuta Saito (Hanjuku-kaso Co., Ltd. / Cornell University)

We are looking for more contributors and maintainers! Please feel free to pull requests.

format:
- [title](paper link) [links]
  - author1, author2, and author3. arXiv/conferences/journals/, year.

For any question, feel free to contact: [email protected]

• Papers
  • Review/Survey/Position Papers
  • Offline RL: Theory/Methods
  • Offline RL: Benchmarks/Experiments/Applications
  • Off-Policy Evaluation: Theory/Methods
    • Contextual Bandits
    • Reinforcement Learning
  • Off-Policy Evaluation: Benchmarks/Experiments/Applications
• Open Source Software/Implementations
• Blog/Podcast
  • Blog
  • Podcast
• Related Workshops
• Tutorials/Talks/Lectures

• A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
  • Rafael Figueiredo Prudencio, Marcos R. O. A. Maximo, and Esther Luna Colombini. arXiv, 2022.
• Deep Reinforcement Learning: Opportunities and Challenges
  • Yuxi Li. arXiv, 2022.
• A Survey on Model-based Reinforcement Learning
  • Fan-Ming Luo, Tian Xu, Hang Lai, Xiong-Hui Chen, Weinan Zhang, and Yang Yu. arXiv, 2022.
• Survey on Fair Reinforcement Learning: Theory and Practice
  • Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, and Mykola Pechenizkiy. arXiv, 2022.
• Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
  • Haruka Kiyohara, Kosuke Kawakami, and Yuta Saito. arXiv, 2021.
• A Survey of Generalisation in Deep Reinforcement Learning
  • Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. arXiv, 2021.
• Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
  • Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. arXiv, 2020.

• Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
  • Zeren Huang, Wenhao Chen, Weinan Zhang, Chuhan Shi, Furui Liu, Hui-Ling Zhen, Mingxuan Yuan, Jianye Hao, Yong Yu, and Jun Wang. arXiv, 2022.
• Offline Reinforcement Learning at Multiple Frequencies [webpage]
  • Kaylee Burns, Tianhe Yu, Chelsea Finn, and Karol Hausman. arXiv, 2022.
• Offline Equilibrium Finding
  • Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Hau Chan, and Bo An. arXiv, 2022.
• General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
  • Francesco Faccio, Aditya Ramesh, Vincent Herrmann, Jean Harb, and Jürgen Schmidhuber. arXiv, 2022.
• When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
  • Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, and Xianyuan Zhan. arXiv, 2022.
• Behavior Transformers: Cloning k modes with one stone
  • Nur Muhammad Mahi Shafiullah, Zichen Jeff Cui, Ariuntuya Altanzaya, and Lerrel Pinto. arXiv, 2022.
• Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination
  • Jiafei Lyu, Xiu Li, and Zongqing Lu. arXiv, 2022.
• Bootstrapped Transformer for Offline Reinforcement Learning
  • Kerong Wang, Hanye Zhao, Xufang Luo, Kan Ren, Weinan Zhang, and Dongsheng Li. arXiv, 2022.
• Contrastive Learning as Goal-Conditioned Reinforcement Learning
  • Benjamin Eysenbach, Tianjun Zhang, Ruslan Salakhutdinov, and Sergey Levine. arXiv, 2022.
• Federated Offline Reinforcement Learning
  • Doudou Zhou, Yufeng Zhang, Aaron Sonabend-W, Zhaoran Wang, Junwei Lu, and Tianxi Cai. arXiv, 2022.
• Mildly Conservative Q-Learning for Offline Reinforcement Learning
  • Jiafei Lyu, Xiaoteng Ma, Xiu Li, and Zongqing Lu. arXiv, 2022.
• Provable Benefit of Multitask Representation Learning in Reinforcement Learning
  • Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, and Yingbin Liang. arXiv, 2022
• Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward
  • Tengyu Xu and Yingbin Liang. arXiv, 2022.
• Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning
  • Deyao Zhu, Li Erran Li, and Mohamed Elhoseiny. arXiv, 2022.
• Model-Based Reinforcement Learning Is Minimax-Optimal for Offline Zero-Sum Markov Games
  • Yuling Yan, Gen Li, Yuxin Chen, and Jianqing Fan. arXiv, 2022.
• RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
  • Rui Yang, Chenjia Bai, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, and Lei Han. arXiv, 2022.
• Offline Reinforcement Learning with Causal Structured World Models
  • Zheng-Mao Zhu, Xiong-Hui Chen, Hong-Long Tian, Kun Zhang, and Yang Yu. arXiv, 2022.
• Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning
  • David Brandfonbrener, Remi Tachet des Combes, and Romain Laroche. arXiv, 2022.
• When does return-conditioned supervised learning work for offline reinforcement learning?
  • David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, and Joan Bruna. arXiv, 2022.
• Offline Reinforcement Learning with Differential Privacy
  • Dan Qiao and Yu-Xiang Wang. arXiv, 2022.
• Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL
  • Wonjoon Goo and Scott Niekum. arXiv, 2022.
• Byzantine-Robust Online and Offline Distributed Reinforcement Learning
  • Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, and Xiaojin Zhu. arXiv, 2022.
• Model Generation with Provable Coverability for Offline Reinforcement Learning
  • Chengxing Jia, Hao Yin, Chenxiao Gao, Tian Xu, Lei Yuan, Zongzhang Zhang, and Yang Yu. arXiv, 2022.
• On Gap-dependent Bounds for Offline Reinforcement Learning
  • Xinqi Wang, Qiwen Cui, and Simon S. Du. arXiv, 2022.
• Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
  • Qiwen Cui and Simon S. Du. arXiv, 2022.
• You Can't Count on Luck: Why Decision Transformers Fail in Stochastic Environments
  • Keiran Paster, Sheila McIlraith, and Jimmy Ba. arXiv, 2022.
• Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
  • Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, and Tong Zhang. arXiv, 2022.
• Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
  • Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, and Ofir Nachum. arXiv, 2022.
• Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
  • Miao Lu, Yifei Min, Zhaoran Wang, and Zhuoran Yang. arXiv, 2022.
• Multi-Game Decision Transformers
  • Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, and Igor Mordatch. arXiv, 2022.
• Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
  • Jinning Li, Chen Tang, Masayoshi Tomizuka, and Wei Zhan. arXiv, 2022.
• Distance-Sensitive Offline Reinforcement Learning
  • Jianxiong Li, Xianyuan Zhan, Haoran Xu, Xiangyu Zhu, Jingjing Liu, and Ya-Qin Zhang. arXiv, 2022.
• User-Interactive Offline Reinforcement Learning
  • Phillip Swazinna, Steffen Udluft, and Thomas Runkler. arXiv, 2022.
• Pessimism for Offline Linear Contextual Bandits using ℓp Confidence Sets
  • Gene Li, Cong Ma, and Nathan Srebro. arXiv, 2022.
• No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
  • Han Wang, Archit Sakhadeo, Adam White, James Bell, Vincent Liu, Xutong Zhao, Puer Liu, Tadashi Kozuno, Alona Fyshe, and Martha White. arXiv, 2022.
• How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
  • Alex X. Lee, Coline Devin, Jost Tobias Springenberg, Yuxiang Zhou, Thomas Lampe, Abbas Abdolmaleki, and Konstantinos Bousmalis. arXiv, 2022.
• Offline Visual Representation Learning for Embodied Navigation
  • Karmesh Yadav, Ram Ramrakhya, Arjun Majumdar, Vincent-Pierre Berges, Sachit Kuhar, Dhruv Batra, Alexei Baevski, and Oleksandr Maksymets. arXiv, 2022.
• Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers
  • Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca Dragan, and Sam Devlin. arXiv, 2022.
• RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
  • Marc Rigter, Bruno Lacerda, and Nick Hawes. arXiv, 2022.
• BATS: Best Action Trajectory Stitching
  • Ian Char, Viraj Mehta, Adam Villaflor, John M. Dolan, Jeff Schneider. arXiv, 2022.
• Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
  • Gen Li, Laixi Shi, Yuxin Chen, Yuejie Chi, and Yuting Wei. arXiv, 2022.
• PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
  • Tong Sang, Hongyao Tang, Yi Ma, Jianye Hao, Yan Zheng, Zhaopeng Meng, Boyan Li, and Zhen Wang. arXiv, 2022.
• Jump-Start Reinforcement Learning [website]
  • Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, and Karol Hausman. arXiv, 2022.
• Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps
  • Jinglin Chen and Nan Jiang. arXiv, 2022.
• Bellman Residual Orthogonalization for Offline Reinforcement Learning
  • Andrea Zanette, and Martin J. Wainwright. arXiv, 2022.
• Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
  • Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Yuan Gao, Jianhao Wang, Wenzhe Li, Bin Liang, Chelsea Finn, and Chongjie Zhang. arXiv, 2022.
• Meta Reinforcement Learning for Adaptive Control: An Offline Approach
  • Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom, Philip D. Loewen, Michael G. Forbes, and R. Bhushan Gopaluni. arXiv, 2022.
• The Efficacy of Pessimism in Asynchronous Q-Learning
  • Yuling Yan, Gen Li, Yuxin Chen, and Jianqing Fan. arXiv, 2022.
• Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation
  • Yunhan Huang and Quanyan Zhu. arXiv, 2022.
• A Regularized Implicit Policy for Offline Reinforcement Learning
  • Shentao Yang, Zhendong Wang, Huangjie Zheng, Yihao Feng, and Mingyuan Zhou. arXiv, 2022.
• Reinforcement Learning in Possibly Nonstationary Environments [code]
  • Mengbing Li, Chengchun Shi, Zhenke Wu, and Piotr Fryzlewicz. arXiv, 2022.
• Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons
  • Chengchun Shi, Shikai Luo, Hongtu Zhu, and Rui Song. arXiv, 2022.
• VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
  • Che Wang, Xufang Luo, Keith Ross, and Dongsheng Li. arXiv, 2022.
• Retrieval-Augmented Reinforcement Learning
  • Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, and Charles Blundell. arXiv, 2022.
• Supported Policy Optimization for Offline Reinforcement Learning
  • Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, and Mingsheng Long. arXiv, 2022.
• Online Decision Transformer
  • Qinqing Zheng, Amy Zhang, and Aditya Grover. arXiv, 2022.
• Transferred Q-learning
  • Elynn Y. Chen, Michael I. Jordan, and Sai Li. arXiv, 2022.
• Settling the Communication Complexity for Distributed Offline Reinforcement Learning
  • Juliusz Krysztof Ziomek, Jun Wang, and Yaodong Yang. arXiv, 2022.
• Offline Reinforcement Learning with Realizability and Single-policy Concentrability
  • Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, and Jason D. Lee. arXiv, 2022.
• Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
  • Rui Yang, Yiming Lu, Wenzhe Li, Hao Sun, Meng Fang, Yali Du, Xiu Li, Lei Han, and Chongjie Zhang. arXiv, 2022.
• Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning
  • Jing Dong and Xin T. Tong. arXiv, 2022.
• Can Wikipedia Help Offline Reinforcement Learning?
  • Machel Reid, Yutaro Yamada, and Shixiang Shane Gu. arXiv, 2022.
• MOORe: Model-based Offline-to-Online Reinforcement Learning
  • Yihuan Mao, Chao Wang, Bin Wang, and Chongjie Zhang. arXiv, 2022.
• Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning
  • Ziyang Tang, Yihao Feng, and Qiang Liu. arXiv, 2022.
• Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
  • Samin Yeasar Arnob, Riashat Islam, and Doina Precup. arXiv, 2022.
• Single-Shot Pruning for Offline Reinforcement Learning
  • Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, and Doina Precup. arXiv, 2022.
• Offline RL Policies Should be Trained to be Adaptive
  • Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, and Sergey Levine. ICML, 2022.
• Adversarially Trained Actor Critic for Offline Reinforcement Learning
  • Ching-An Cheng, Tengyang Xie, Nan Jiang, and Alekh Agarwal. ICML, 2022.
• Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
  • Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, and Zhuoran Yang. ICML, 2022.
• How to Leverage Unlabeled Data in Offline Reinforcement Learning
  • Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, and Sergey Levine. ICML, 2022.
• Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
  • Ling Pan, Longbo Huang, Tengyu Ma, and Huazhe Xu. ICML, 2022.
• Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
  • Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, and Bo An. ICML, 2022.
• Offline Meta-Reinforcement Learning with Online Self-Supervision
  • Vitchyr H. Pong, Ashvin Nair, Laura Smith, Catherine Huang, and Sergey Levine. ICML, 2022.
• Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
  • Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman, and Osbert Bastani. ICML, 2022.
• Constrained Offline Policy Optimization
  • Nicholas Polosky, Bruno C. Da Silva, Madalina Fiterau, and Jithin Jagannath. ICML, 2022.
• Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
  • Haoran Xu, Xianyuan Zhan, Honglei Yin, and Huiling Qin. ICML, 2022.
• Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
  • Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, and Zhaoran Wang. ICML, 2022.
• Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
  • Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, and Yuejie Chi. ICML, 2022.
• Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
  • Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, and Wen Sun. ICML, 2022.
• Interpretable Off-Policy Learning via Hyperbox Search
  • Daniel Tschernutter, Tobias Hatt, and Stefan Feuerriegel. ICML, 2022.
• Prompting Decision Transformer for Few-Shot Policy Generalization
  • Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, and Chuang Gan. ICML, 2022.
• Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
  • Shentao Yang, Yihao Feng, Shujian Zhang, and Mingyuan Zhou. ICML, 2022.
• On the Role of Discount Factor in Offline Reinforcement Learning
  • Hao Hu, Yiqin Yang, Qianchuan Zhao, and Chongjie Zhang. ICML, 2022.
• Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
  • Matthias Weissenbacher, Samarth Sinha, Animesh Garg, and Yoshinobu Kawahara. ICML, 2022.
• Representation Learning for Online and Offline RL in Low-rank MDPs [video]
  • Masatoshi Uehara, Xuezhou Zhang, and Wen Sun. ICLR, 2022.
• Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage [video]
  • Masatoshi Uehara and Wen Sun. ICLR, 2022.
• Revisiting Design Choices in Model-Based Offline Reinforcement Learning
  • Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, and Stephen J. Roberts. ICLR, 2022.
• DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
  • Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, and Sergey Levine. ICLR, 2022.
• COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
  • Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, and Arthur Guez. ICLR, 2022.
• POETREE: Interpretable Policy Learning with Adaptive Decision Trees
  • Alizée Pace, Alex J. Chan, and Mihaela van der Schaar. ICLR, 2022.
• Planning in Stochastic Environments with a Learned Model
  • Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K Hubert, and David Silver. ICLR, 2022.
• Offline Reinforcement Learning with Value-based Episodic Memory
  • Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, and Bin Liang. ICLR, 2022.
• Should I Run Offline Reinforcement Learning or Behavioral Cloning?
  • Aviral Kumar, Joey Hong, Anikait Singh, and Sergey Levine. ICLR, 2022.
• Learning Value Functions from Undirected State-only Experience [website] [code]
  • Matthew Chang, Arjun Gupta, and Saurabh Gupta. ICLR, 2022.
• Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL
  • Rui Yang, Yiming Lu, Wenzhe Li, Hao Sun, Meng Fang, Yali Du, Xiu Li, Lei Han, and Chongjie Zhang. ICLR, 2022.
• Offline Reinforcement Learning with Implicit Q-Learning
  • Ilya Kostrikov, Ashvin Nair, and Sergey Levine. ICLR, 2022.
• RvS: What is Essential for Offline RL via Supervised Learning?
  • Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, and Sergey Levine. ICLR, 2022.
• Pareto Policy Pool for Model-based Offline Reinforcement Learning
  • Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, and Yuhui Shi. ICLR, 2022.
• CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning
  • Matthias Gerstgrasser, Rakshit Trivedi, and David C. Parkes. ICLR, 2022.
• COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
  • Fan Wu, Linyi Li, Chejian Xu, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, and Bo Li. ICLR, 2022.
• DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
  • Jinxin Liu, Hongyin Zhang, and Donglin Wang. ICLR, 2022.
• Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
  • Ming Yin, Yaqi Duan, Mengdi Wang, and Yu-Xiang Wang. ICLR, 2022.
• Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
  • Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang. ICLR, 2022.
• Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
  • Thanh Nguyen-Tang, Sunil Gupta, A.Tuan Nguyen, and Svetha Venkatesh. ICLR, 2022.
• Generalized Decision Transformer for Offline Hindsight Information Matching [website]
  • Hiroki Furuta, Yutaka Matsuo, and Shixiang Shane Gu. ICLR, 2022.
• Model-Based Offline Meta-Reinforcement Learning with Regularization
  • Sen Lin, Jialin Wan, Tengyu Xu, Yingbin Liang, and Junshan Zhang. ICLR, 2022.
• Offline Policy Optimization with Eligible Actions
  • Yao Liu, Yannis Flet-Berliac, and Emma Brunskill. UAI, 2022.
• Towards Off-Policy Learning for Ranking Policies with Logged Feedback
  • Teng Xiao and Suhang Wang. AAAI, 2022.
• Towards Robust Off-policy Learning for Runtime Uncertainty
  • Da Xu, Yuting Ye, Chuanwei Ruan, and Bo Yang. AAAI, 2022.
• Safe Offline Reinforcement Learning Through Hierarchical Policies
  • Shaofan Liu and Shiliang Sun. PAKDD, 2022.
• Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks
  • Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, and Svetha Venkatesh. arXiv, 2021.
• Model Selection in Batch Policy Optimization
  • Jonathan N. Lee, George Tucker, Ofir Nachum, and Bo Dai. arXiv, 2021.
• Learning Contraction Policies from Offline Data
  • Navid Rezazadeh, Maxwell Kolarich, Solmaz S. Kia, and Negar Mehr. arXiv, 2021.
• CoMPS: Continual Meta Policy Search
  • Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine. arXiv, 2021.
• MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
  • Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan, Suraj Nair, Julian Ibarz, Jie Tan, Chelsea Finn, Ion Stoica, and Ken Goldberg. arXiv, 2021.
• Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Conquers All StarCraftII Tasks
  • Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xiyun Li, Weinan Zhang, Ying Wen, Haifeng Zhang, Jun Wang, and Bo Xu. arXiv, 2021.
• Generalizing Off-Policy Learning under Sample Selection Bias
  • Tobias Hatt, Daniel Tschernutter, and Stefan Feuerriegel. arXiv, 2021.
• Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
  • Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson. arXiv, 2021.
• Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms
  • Yanwei Jia and Xun Yu Zhou. arXiv, 2021.
• Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation [video]
  • Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, and Yunzong Xu. arXiv, 2021.
• UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning
  • Christopher Diehl, Timo Sievernich, Martin Krüger, Frank Hoffmann, and Torsten Bertran. arXiv, 2021.
• Exploiting Action Impact Regularity and Partially Known Models for Offline Reinforcement Learning
  • Vincent Liu, James Wright, and Martha White. arXiv, 2021.
• Batch Reinforcement Learning from Crowds
  • Guoxi Zhang and Hisashi Kashima. arXiv, 2021.
• SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning
  • Zhihong Deng, Zuyue Fu, Lingxiao Wang, Zhuoran Yang, Chenjia Bai, Zhaoran Wang, and Jing Jiang. arXiv, 2021.
• Safely Bridging Offline and Online Reinforcement Learning
  • Wanqiao Xu, Kan Xu, Hamsa Bastani, and Osbert Bastani. arXiv, 2021.
• Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information
  • Jin Li, Xianyuan Zhan, Zixu Xiao, and Guyue Zhou. arXiv, 2021.
• Value Penalized Q-Learning for Recommender Systems
  • Chengqian Gao, Ke Xu, and Peilin Zhao. arXiv, 2021.
• Offline Reinforcement Learning with Soft Behavior Regularization
  • Haoran Xu, Xianyuan Zhan, Jianxiong Li, and Honglei Yin. arXiv, 2021.
• Planning from Pixels in Environments with Combinatorially Hard Search Spaces
  • Marco Bagatella, Mirek Olšák, Michal Rolínek, and Georg Martius. arXiv, 2021.
• StARformer: Transformer with State-Action-Reward Representations
  • Jinghuan Shang and Michael S. Ryoo. arXiv, 2021.
• Offline RL With Resource Constrained Online Deployment [code]
  • Jayanth Reddy Regatti, Aniket Anand Deshmukh, Frank Cheng, Young Hun Jung, Abhishek Gupta, and Urun Dogan. arXiv, 2021.
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [website]
  • Annie Xie and Chelsea Finn. arXiv, 2021.
• Dual Behavior Regularized Reinforcement Learning
  • Chapman Siu, Jason Traish, and Richard Yi Da Xu. arXiv, 2021.
• DCUR: Data Curriculum for Teaching via Samples with Reinforcement Learning [website] [code]
  • Daniel Seita, Abhinav Gopal, Zhao Mandi, and John Canny. arXiv, 2021.
• DROMO: Distributionally Robust Offline Model-based Policy Optimization
  • Ruizhen Liu, Dazhi Zhong, and Zhicong Chen. arXiv, 2021.
• Implicit Behavioral Cloning
  • Pete Florence, Corey Lynch, Andy Zeng, Oscar Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson. arXiv, 2021.
• Reducing Conservativeness Oriented Offline Reinforcement Learning
  • Hongchang Zhang, Jianzhun Shao, Yuhang Jiang, Shuncheng He, and Xiangyang Ji. arXiv, 2021.
• Policy Gradients Incorporating the Future
  • David Venuto, Elaine Lau, Doina Precup, and Ofir Nachum. arXiv, 2021.
• Offline Decentralized Multi-Agent Reinforcement Learning
  • Jiechuan Jiang and Zongqing Lu. arXiv, 2021.
• OPAL: Offline Preference-Based Apprenticeship Learning [website]
  • Daniel Shin and Daniel S. Brown. arXiv, 2021.
• Constraints Penalized Q-Learning for Safe Offline Reinforcement Learning
  • Haoran Xu, Xianyuan Zhan, and Xiangyu Zhu. arXiv, 2021.
• Where is the Grass Greener? Revisiting Generalized Policy Iteration for Offline Reinforcement Learning
  • Lionel Blondé and Alexandros Kalousis. arXiv, 2021.
• The Least Restriction for Offline Reinforcement Learning
  • Zizhou Su. arXiv, 2021.
• Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
  • Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, and Jinwoo Shin. arXiv, 2021.
• Causal Reinforcement Learning using Observational and Interventional Data
  • Maxime Gasse, Damien Grasset, Guillaume Gaudron, and Pierre-Yves Oudeyer. arXiv, 2021.
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• Policy Evaluation and Optimization with Continuous Treatments
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• On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
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• Learning Bellman Complete Representations for Offline Policy Evaluation
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• Supervised Off-Policy Ranking
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• Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
  • Nathan Kallus, Xiaojie Mao, Kaiwen Wang, and Zhengyuan Zhou. ICML, 2022.
• Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
  • Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, and Mengdi Wang. ICML, 2022.
• Off-Policy Risk Assessment for Markov Decision Processes
  • Audrey Huang, Liu Leqi, Zachary Lipton, and Kamyar Azizzadenesheli. AISTATS, 2022.
• Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
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• Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
  • Xiang Ji, Minshuo Chen, Mengdi Wang, and Tuo Zhao. arXiv, 2022.
• A Sharp Characterization of Linear Estimators for Offline Policy Evaluation
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• A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets [code]
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• A Theoretical Framework of Almost Hyperparameter-free Hyperparameter Selection Methods for Offline Policy Evaluation
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• SOPE: Spectrum of Off-Policy Estimators
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• Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
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• Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
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• Variance-Aware Off-Policy Evaluation with Linear Function Approximation
  • Yifei Min, Tianhao Wang, Dongruo Zhou, and Quanquan Gu. NeurIPS, 2021.
• Universal Off-Policy Evaluation
  • Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, and Philip S. Thomas. NeurIPS, 2021.
• Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
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• State Relevance for Off-Policy Evaluation
  • Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, and Finale Doshi-Velez. ICML, 2021.
• Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
  • Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvari, and Mengdi Wang. ICML, 2021.
• Deeply-Debiased Off-Policy Interval Estimation
  • Chengchun Shi, Runzhe Wan, Victor Chernozhukov, and Rui Song. ICML, 2021.
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
  • Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi. ICLR, 2021.
• Minimax Model Learning
  • Cameron Voloshin, Nan Jiang, and Yisong Yue. AISTATS, 2021.
• Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
  • Andrew Bennett, Nathan Kallus, Lihong Li, and Ali Mousavi. AISTATS, 2021.
• Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes
  • Andrew Bennett and Nathan Kallus. arXiv, 2021.
• Off-Policy Evaluation in Partially Observed Markov Decision Processes
  • Yuchen Hu and Stefan Wager. arXiv, 2021.
• A Spectral Approach to Off-Policy Evaluation for POMDPs
  • Yash Nair and Nan Jiang. arXiv, 2021.
• Projected State-action Balancing Weights for Offline Reinforcement Learnings
  • Jiayi Wang, Zhengling Qi, and Raymond K.W. Wong. arXiv, 2021.
• Active Offline Policy Selection
  • Ksenia Konyushkova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, and Nando de Freitas. arXiv, 2021.
• On Instrumental Variable Regression for Deep Offline Policy Evaluation
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• Characterizing Uniform Convergence in Offline Policy Evaluation via model-based approach: Offline Learning, Task-Agnostic and Reward-Free
  • Ming Yin and Yu-Xiang Wang. arXiv, 2021.
• Average-Reward Off-Policy Policy Evaluation with Function Approximation
  • Shangtong Zhang, Yi Wan, Richard S. Sutton, and Shimon Whiteson. arXiv, 2021.
• Sequential causal inference in a single world of connected units
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• Offline Policy Selection under Uncertainty
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• Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
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• Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies
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• Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning
  • Rahul Singh, Liyuan Xu, and Arthur Gretton. arXiv, 2020.
• Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
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• CoinDICE: Off-Policy Confidence Interval Estimation
  • Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvari, and Dale Schuurmans. NeurIPS, 2020.
• Off-Policy Interval Estimation with Lipschitz Value Iteration
  • Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, and Qiang Liu. NeurIPS, 2020.
• Off-Policy Evaluation via the Regularized Lagrangian
  • Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, and Dale Schuurmans. NeurIPS, 2020.
• Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
  • Nan Jiang and Jiawei Huang. NeurIPS, 2020.
• Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation
  • Ilya Kostrikov and Ofir Nachum. arXiv, 2020.
• Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control [video]
  • Bingqing Chen, Ming Jin, Zhe Wang, Tianzhen Hong, and Mario Bergés, RLEM, 2020.
• Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies
  • Xinyun Chen, Lu Wang, Yizhe Hang, Heng Ge, and Hongyuan Zha. ICLR, 2020.
• Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
  • Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, and Qiang Liu. ICLR, 2020.
• Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
  • Ali Mousavi, Lihong Li, Qiang Liu, and Denny Zhou. ICLR, 2020.
• Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
  • Yaqi Duan, Zeyu Jia, and Mengdi Wang. ICML, 2020.
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
  • Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi, Emma Brunskill, and Finale Doshi-Velez. ICML, 2020.
• Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
  • Nathan Kallus and Masatoshi Uehara. ICML, 2020.
• Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
  • Yao Liu, Pierre-Luc Bacon, and Emma Brunskill. ICML, 2020.
• Minimax Weight and Q-Function Learning for Off-Policy Evaluation
  • Masatoshi Uehara, Jiawei Huang, and Nan Jiang. ICML, 2020.
• Accountable Off-Policy Evaluation With Kernel Bellman Statistics
  • Yihao Feng, Tongzheng Ren, Ziyang Tang, and Qiang Liu. ICML, 2020.
• Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning
  • Ming Yin and Yu-Xiang Wang. ICML, 2020.
• Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning
  • Nathan Kallus and Masatoshi Uehara. arXiv, 2019.
• Off-Policy Evaluation in Partially Observable Environments
  • Guy Tennenholtz, Uri Shalit, and Shie Mannor. AAAI, 2019.
• Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning
  • Nathan Kallus and Masatoshi Uehara. NeurIPS, 2019.
• Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
  • Tengyang Xie, Yifei Ma, and Yu-Xiang Wang. NeuIPS, 2019.
• Off-Policy Evaluation via Off-Policy Classification
  • Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, and Sergey Levine. NeuIPS, 2019.
• Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
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• Batch Policy Learning under Constraints [code] [website]
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• More Efficient Off-Policy Evaluation through Regularized Targeted Learning
  • Aurelien Bibaut, Ivana Malenica, Nikos Vlassis, and Mark Van Der Laan. ICML, 2019.
• Combining parametric and nonparametric models for off-policy evaluation
  • Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, and Finale Doshi-Velez. ICML, 2019.
• Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
  • Michael Oberst and David Sontag. ICML, 2019.
• Importance Sampling Policy Evaluation with an Estimated Behavior Policy
  • Josiah Hanna, Scott Niekum, and Peter Stone. ICML, 2019.
• When People Change their Mind: Off-Policy Evaluation in Non-Stationary Recommendation Environments
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• Representation Balancing MDPs for Off-policy Policy Evaluation
  • Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, and Emma Brunskill. NeuIPS, 2018.
• Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
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• Confounding-Robust Policy Improvement
  • Nathan Kallus and Angela Zhou. NeuIPS, 2018.
• Balanced Policy Evaluation and Learning
  • Nathan Kallus. NeuIPS, 2018.
• More Robust Doubly Robust Off-policy Evaluation
  • Mehrdad Farajtabar, Yinlam Chow, and Mohammad Ghavamzadeh. ICML, 2018.
• Importance Sampling for Fair Policy Selection
  • Shayan Doroudi, Philip Thomas, and Emma Brunskill. UAI, 2017.
• Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing
  • Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, and Emma Brunskill. AAAI, 2017.
• Consistent On-Line Off-Policy Evaluation
  • Assaf Hallak and Shie Mannor. ICML, 2017.
• Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation
  • Josiah P. Hanna, Peter Stone, and Scott Niekum. AAAMS, 2016.
• Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
  • Nan Jiang and Lihong Li. ICML, 2016.
• Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
  • Philip Thomas and Emma Brunskill. ICML, 2016.
• High Confidence Policy Improvement
  • Philip Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. ICML, 2015.
• High Confidence Off-Policy Evaluation
  • Philip S. Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh. AAAI, 2015.
• Eligibility Traces for Off-Policy Policy Evaluation
  • Doina Precup, Richard S. Sutton, and Satinder P. Singh. ICML, 2000.

• Offline Policy Comparison with Confidence: Benchmarks and Baselines
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• Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation [software] [public dataset]
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• Evaluating the Robustness of Off-Policy Evaluation [software]
  • Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno. RecSys, 2021.
• Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service
  • Yuta Saito, Takuma Udagawa, and Kei Tateno. arXiv, 2021.
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
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• Benchmarks for Deep Off-Policy Evaluation [code]
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• Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings
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• Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning [code]
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• Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling
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• Offline Evaluation to Make Decisions About Playlist Recommendation
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• Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
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• Evaluating Reinforcement Learning Algorithms in Observational Health Settings
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• Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems
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• Offline A/B testing for Recommender Systems
  • Alexandre Gilotte, Clément Calauzènes, Thomas Nedelec, Alexandre Abraham, and Simon Dollé. WSDM, 2018.
• Offline Comparative Evaluation with Incremental, Minimally-Invasive Online Feedback
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• Handling Confounding for Realistic Off-Policy Evaluation
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• Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
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• Open Bandit Pipeline: a research framework for bandit algorithms and off-policy evaluation [paper] [documentation] [dataset]
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• pyIEOE: Towards An Interpretable Evaluation for Offline Evaluation [paper]
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• d3rlpy: An Offline Deep Reinforcement Learning Library [paper] [website] [documentation]
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• MINERVA: An out-of-the-box GUI tool for data-driven deep reinforcement learning [website] [documentation]
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• DICE: The DIstribution Correction Estimation Library [paper1] [paper2] [paper3]
• Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li, Ruiyi Zhang, Dale Schuurmans.
• RLDS: Reinforcement Learning Datasets [paper]
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• Benchmarks for Deep Off-Policy Evaluation [paper]
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• D4RL: Datasets for Deep Data-Driven Reinforcement Learning [paper] [website]
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• RL Unplugged: Benchmarks for Offline Reinforcement Learning [paper] [dataset]
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• RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System [paper] dataset]
• NeoRL: Near Real-World Benchmarks for Offline Reinforcement Learning [paper] [website]
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• The Industrial Benchmark Offline RL Datasets [paper]
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• RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising [paper]
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• MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces [paper] [documantation]
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• Counterfactual Evaluation for Recommendation Systems
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• Offline Reinforcement Learning: How Conservative Algorithms Can Enable New Applications
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• AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
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• D4RL: Building Better Benchmarks for Offline Reinforcement Learning
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• Does On-Policy Data Collection Fix Errors in Off-Policy Reinforcement Learning?
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• Tackling Open Challenges in Offline Reinforcement Learning
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• An Optimistic Perspective on Offline Reinforcement Learning
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• Decisions from Data: How Offline Reinforcement Learning Will Change How We Use Machine Learning
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• Introducing completely free datasets for data-driven deep reinforcement learning
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• Offline (Batch) Reinforcement Learning: A Review of Literature and Applications
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• Data-Driven Deep Reinforcement Learning
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• Sergey Levine on Robot Learning & Offline RL
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• Off-Line, Off-Policy RL for Real-World Decision Making at Facebook
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• Xianyuan Zhan | TalkRL: The Reinforcement Learning Podcast
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• MOReL: Model-Based Offline Reinforcement Learning with Aravind Rajeswaran
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• Trends in Reinforcement Learning with Chelsea Finn
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• Nan Jiang | TalkRL: The Reinforcement Learning Podcast
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• Scott Fujimoto | TalkRL: The Reinforcement Learning Podcast
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• Offline Reinforcement Learning (NeurIPS 2022)
• Reinforcement Learning for Real Life (NeurIPS 2022)
• Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning (RecSys 2022)
• Offline Reinforcement Learning (NeurIPS 2021)
• Reinforcement Learning for Real Life (ICML 2021)
• Reinforcement Learning Day 2021
• Offline Reinforcement Learning (NeurIPS 2020)
• Reinforcement Learning from Batch Data and Simulation
• Reinforcement Learning for Real Life (RL4RealLife 2020)
• Safety and Robustness in Decision Making (NeurIPS 2019)
• Reinforcement Learning for Real Life (ICML 2019)
• Real-world Sequential Decision Making (ICML 2019)

• Counterfactual Evaluation and Learning for Interactive Systems
  • Yuta Saito and Thorsten Joachims. KDD2022.
• Representation Learning for Online and Offline RL in Low-rank MDPs
  • Masatoshi Uehara. RL Theory Seminar2022.
• Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
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• Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
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• Planning with Reinforcement Learning
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• Imitation learning vs. offline reinforcement learning
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• Tutorial on the Foundations of Offline Reinforcement Learning
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• Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances [website]
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• Offline Reinforcement Learning
  • Sergey Levine. BayLearn2021.
• Offline Reinforcement Learning
  • Guy Tennenholtz. CHIL2021.
• Fast Rates for the Regret of Offline Reinforcement Learning
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• Bellman-consistent Pessimism for Offline Reinforcement Learning
  • Tengyan Xie. RL Theory Seminar2021.
• Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
  • Masatoshi Uehara. RL Theory Seminar2021.
• Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
  • Paria Rashidinejad. RL Theory Seminar2021.
• Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm
  • Lin Chen. RL Theory Seminar2021.
• Is Pessimism Provably Efficient for Offline RL?
  • Ying Jin. RL Theory Seminar2021.
• Adaptive Estimator Selection for Off-Policy Evaluation
  • Yi Su. RL Theory Seminar2021.
• What are the Statistical Limits of Offline RL with Linear Function Approximation?
  • Ruosong Wang. RL Theory Seminar2021.
• Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
  • Andrea Zanette. RL Theory Seminar2021.
• A Gentle Introduction to Offline Reinforcement Learning
  • Sergey Levine. 2021.
• Principles for Tackling Distribution Shift: Pessimism, Adaptation, and Anticipation
  • Chelsea Finn. 2020-2021 Machine Learning Advances and Applications Seminar.
• Offline Reinforcement Learning: Incorporating Knowledge from Data into RL
  • Sergey Levine. IJCAI-PRICAI2020 Knowledge Based Reinforcement Learning Workshop.
• Offline RL
  • Nando de Freitas. NeurIPS2020 OfflineRL Workshop.
• Learning a Multi-Agent Simulator from Offline Demonstrations
  • Brandyn White. NeurIPS2020 OfflineRL Workshop.
• Towards Reliable Validation and Evaluation for Offline RL
  • Nan Jiang. NeurIPS2020 OfflineRL Workshop.
• Batch RL Models Built for Validation
  • Finale Doshi-Velez. NeurIPS2020 OfflineRL Workshop.
• Offline Reinforcement Learning: From Algorithms to Practical Challenges
  • Aviral Kumar and Sergey Levine. NeurIPS2020.
• Data Scalability for Robot Learning
  • Chelsea Finn. RI Seminar2020.
• Statistically Efficient Offline Reinforcement Learning
  • Nathan Kallus. ARL Seminor2020.
• Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning
  • Yu-Xiang Wang. RL Theory Seminar2020.
• Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
  • Mengdi Wang. RL Theory Seminar2020.
• Beyond the Training Distribution: Embodiment, Adaptation, and Symmetry
  • Chelsea Finn. EI Seminar2020.
• Combining Statistical methods with Human Input for Evaluation and Optimization in Batch Settings
  • Finale Doshi-Velez. NeurIPS2019 Workshop on Safety and Robustness in Decision Making.
• Efficiently Breaking the Curse of Horizon with Double Reinforcement Learning
  • Nathan Kallus. NeurIPS2019 Workshop on Safety and Robustness in Decision Making.
• Scaling Probabilistically Safe Learning to Robotics
  • Scott Niekum. NeurIPS2019 Workshop on Safety and Robustness in Decision Making.
• Deep Reinforcement Learning in the Real World
  • Sergey Levine. Workshop on New Directions in Reinforcement Learning and Control2019.