[1] Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi. An optimistic perspective on offline reinforcement learning. In International Conference on Machine Learning, 2020.
[2] Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song. Uncertainty-based offline reinforcement learning with diversified q-ensemble. Advances in neural information processing systems, 34:7436-7447, 2021.
[3] David Brandfonbrener, Will Whitney, Rajesh Ranganath, and Joan Bruna. Offline rl without off-policy evaluation. Advances in neural information processing systems, 34:4933-4946, 2021.
[4] Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision transformer: Reinforcement learning via sequence modeling, 2021.
[5] Ching-An Cheng, Tengyang Xie, Nan Jiang, and Alekh Agarwal. Adversarily trained actor critic for offline reinforcement learning. In International Conference on Machine Learning, pages 3852-3878. PMLR, 2022.
[6] Damien Ernst, Pierre Geurts, and Louis Wehenkel. Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6, 2005.
[7] Rasool Fakoor, Pratik Chaudhari, and Alexander J. Smola. P3o: Policy-on policy-off policy optimization. In Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, volume 115, pages 1017-1027, 2020.
[8] Rasool Fakoor, Jonas W Mueller, Kavosh Asadi, Pratik Chaudhari, and Alexander J Smola. Continuous doubly constrained batch reinforcement learning. Advances in Neural Information Processing Systems, 34:11260-11273, 2021.
[9] Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning. arXiv:2004.07219, 2020.
[10] Yuwei Fu, Di Wu, and Benoit Boulet. A closer look at offline rl agents. Advances in Neural Information Processing Systems, 35:8591-8604, 2022.
[11] Scott Fujimoto and Shixiang Shane Gu. A minimalist approach to offline reinforcement learning. Advances in neural information processing systems, 34:20132-20145, 2021.
[12] Scott Fujimoto, David Meger, and Doina Precup. Off-policy deep reinforcement learning without exploration. In International Conference on Machine Learning, pages 2052-2062, 2019.
[13] Scott Fujimoto, Herke van Hoof, and David Meger. Addressing function approximation error in actor-critic methods. In Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 1587-1596. PMLR, 2018.
[14] Kamyar Ghasemipour, Shixiang Shane Gu, and Ofir Nachum. Why so pessimistic? estimating uncertainties for offline rl through ensembles, and why their independence matters. Advances in Neural Information Processing Systems, 35:18267-18281, 2022.
[15] Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, and Shixiang Shane Gu. Emaq: Expected-max q-learning operator for simple yet effective offline and online rl. arXiv:2007.11091, 2021.
[16] Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, and Sergey Levine. Offline rl policies should be trained to be adaptive. In International Conference on Machine Learning, pages 7513-7530. PMLR, 2022.
[17] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. arXiv:1801.01290, 2018.
[18] Charles A Hepburn and Giovanni Montana. Model-based trajectory stitching for improved offline reinforcement learning. arXiv preprint arXiv:2211.11603, 2022.