[22] Matthieu Geist, Bruno Scherrer, and Olivier Pietquin. A theory of regularized markov decision processes. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 2160-2169. PMLR, 2019.
[23] 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.
[24] Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, and Nando de Freitas. Regularized behavior value estimation. arXiv preprint arXiv:2103.09575, 2021.
[25] Michael Janner, Qiyang Li, and Sergey Levine. Offline reinforcement learning as one big sequence modeling problem. Advances in neural information processing systems, 34, 2021.
[26] Michael Janner, Yilun Du, Joshua B Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991, 2022.
[27] Ying Jin, Zhuoran Yang, and Zhaoran Wang. Is pessimism provably efficient for offline rl? In International Conference on Machine Learning, pages 5084-5096. PMLR, 2021.
[28] Yachen Kang, Diyuan Shi, Jinxin Liu, Li He, and Donglin Wang. Beyond reward: Offline preference-guided policy optimization. arXiv preprint arXiv:2305.16217, 2023.
[29] Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims. Morel: Model-based offline reinforcement learning. Advances in neural information processing systems, 33:21810-21823, 2020.
[30] Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum. Offline reinforcement learning with fisher divergence: critical regularization. In International Conference on Machine Learning, pages 5774-5783. PMLR, 2021.
[31] Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit q-learning. arXiv preprint arXiv:2110.06169, 2021.
[32] Aviral Kumar and Sergey Levine. Model inversion networks for model-based optimization. Advances in Neural Information Processing Systems, 33:5126-5137, 2020.
[33] Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine. Stabilizing off-policy q-learning via bootstrapping error reduction. Advances in Neural Information Processing Systems, 32, 2019.
[34] Aviral Kumar, Xue Bin Peng, and Sergey Levine. Reward-conditioned policies. arXiv preprint arXiv:1912.13465, 2019.
[35] 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.
[36] Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, HAO Jianye, and Ping Luo. Chipformer: Transferable chip placement via offline decision transformer. ICML, 2023. URL https://openreview.net/pdf?id=j0miEWtw87.
[37] Sascha Lange, Thomas Gabel, and Martin Riedmiller. Batch reinforcement learning. In Reinforcement learning, pages 45-73. Springer, 2012.
[38] Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang. A tutorial on energy-based learning. Predicting structured data, 1(0), 2006.
[39] Jongmin Lee, Wonseok Jeon, Byungjun Lee, Joelle Pineau, and Kee-Eung Kim. Optidice: Offline policy optimization via stationary distribution correction estimation. In International Conference on Machine Learning, pages 6120-6130. PMLR, 2021.