[13] Jiechuan Jiang and Zongqing Lu. Offline decentralized multi-agent reinforcement learning. arXiv preprint arXiv:2108.01832, 2021.
[14] Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. Multiagent actor-critic for mixed cooperative-competitive environments. In Advances in Neural Information Processing Systems, volume 30, pages 6379–6390, 2017.
[15] Christian Schroeder de Witt, Bei Peng, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Böhmer, and Shimon Whiteson. Deep multi-agent reinforcement learning for decentralized continuous cooperative control. arXiv preprint arXiv:2003.06709, 19, 2020.
[16] Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, and Shimon Whiteson. The starcraft multi-agent challenge. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pages 2186-2188, 2019.
[17] Yifan Wu, George Tucker, and Ofir Nachum. Behavior regularized offline reinforcement learning. arXiv preprint arXiv:1911.11361, 2019.
[18] Haoran Xu, Xianyuan Zhan, Jianxiong Li, and Honglei Yin. Offline reinforcement learning with soft behavior regularization. arXiv preprint arXiv:2110.07395, 2021.
[19] Peng Cheng, Xianyuan Zhan, Zhihao Wu, Wenjia Zhang, Shoucheng Song, Han Wang, Youfang Lin, and Li Jiang. Look beneath the surface: Exploiting fundamental symmetry for sample-efficient offline rl. Advances in Neural Information Processing Systems, 2023.
[20] Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. Advances in Neural Information Processing Systems, 33:1179-1191, 2020.
[21] Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum. Offline reinforcement learning with fisher divergence critic regularization. In International Conference on Machine Learning, pages 5774-5783. PMLR, 2021.
[22] Haoran Xu, Xianyuan Zhan, and Xiangyu Zhu. Constraints penalized q-learning for safe offline reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 8753-8760, 2022.
[23] Haoyi Niu, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, and Xianyuan Zhan. When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning. Advances in Neural Information Processing Systems, 35:36599-36612, 2022.
[24] Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua Susskind, Jian Zhang, Ruslan Salakhutdinov, and Hanlin Goh. Uncertainty weighted actor-critic for offline reinforcement learning. arXiv preprint arXiv:2105.08140, 2021.
[25] Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang. Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning. In International Conference on Learning Representations, 2021.
[26] Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma. Mopo: Model-based offline policy optimization. Advances in Neural Information Processing Systems, 33:14129-14142, 2020.
[27] Xianyuan Zhan, Haoran Xu, Yue Zhang, Xiangyu Zhu, Honglei Yin, and Yu Zheng. Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 2022.
[28] Xianyuan Zhan, Xiangyu Zhu, and Haoran Xu. Model-based offline planning with trajectory pruning. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022, 2022.
[29] 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.
[30] Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor. A survey and critique of multiagent deep reinforcement learning. Autonomous Agents and Multi-Agent Systems, 33(6):750-797, 2019.