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Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al · 2008
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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An alternative softmax operator for reinforcement learning
Kavosh Asadi and Michael L. Littman · 2017
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Bridging the gap between value and policy based reinforcement learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2017
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, G. Tucker, and Sergey Levine · 2019
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On principled entropy exploration in policy optimization
Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, and Martin Müller · 2019
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Revisiting the softmax Bellman operator: New benefits and new perspective
Zhao Song, Ron Parr, and Lawrence Carin · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, G. Tucker, and Ofir Nachum · 2019
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Maximum entropy monte-carlo planning
Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, and Martin Müller · 2019
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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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D4rl: Datasets for deep data-driven reinforcement learning, 2020
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Advantage weighted regression: Simple and scalable off-policy reinforcement learning
Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2021
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Fuzzy tiling activations: A simple approach to learning sparse representations online
Yangchen Pan, Kirby Banman, and Martha White · 2021
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Uncertainty weighted actor-critic for offline reinforcement learning
Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind, Jian Zhang, Ruslan Salakhutdinov, and Hanlin Goh · 2021
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On the optimality of batch policy optimization algorithms
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, and Dale Schuurmans · 2021
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Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning
Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, and Qianchuan Zhao · 2021
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Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2020
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Plas: Latent action space for offline reinforcement learning
Wenxuan Zhou, Sujay Bajracharya, and David Held · 2020
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Offline RL without off-policy evaluation
David Brandfonbrener, William F Whitney, Rajesh Ranganath, and Joan Bruna · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Gu · 2021
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Emaq: Expected-max q-learning operator for simple yet effective offline and online rl
Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, and Shixiang Shane Gu · 2021
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Is pessimism provably efficient for offline RL?
Ying Jin, Zhuoran Yang, and Zhaoran Wang · 2021
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
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Combo: Conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning
Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang · 2022
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
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Offline reinforcement learning with value-based episodic memory
Xiaoteng Ma, Yiqin Yang, Hao Hu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, Bin Liang, and Qihan Liu · 2022
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Supported policy optimization for offline reinforcement learning
Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, and Mingsheng Long · 2022
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Regularizing a model-based policy stationary distribution to stabilize offline reinforcement learning
Shentao Yang, Yihao Feng, Shujian Zhang, and Mingyuan Zhou · 2022
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