[9] Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, and Jinwoo Shin. Context-aware dynamics model for generalization in model-based reinforcement learning. In ICML, 2020.
[10] Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Yuan Gao, Jianhao Wang, Wenzhe Li, Bin Liang, Chelsea Finn, and Chongjie Zhang. Latent-variable advantage-weighted policy optimization for offline RL. CoRR, abs/2203.08949, 2022.
[11] Wenxuan Zhou, Lerrel Pinto, and Abhinav Gupta. Environment probing interaction policies. In ICLR, 2019.
[12] Xiong-Hui Chen, Yang Yu, Qingyang Li, Fan-Ming Luo, Zhiwei (Tony) Qin, Wenjie Shang, and Jieping Ye. Offline model-based adaptable policy learning. In NeurIPS, 2021.
[13] Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, and Ruslan Salakhutdinov. Off-dynamics reinforcement learning: Training for transfer with domain classifiers. In ICLR, 2021.
[14] Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, and Xianyuan Zhan. When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning. CoRR, abs/2206.13464, 2022.
[15] Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel. Constrained policy optimization. In ICML, 2017.
[16] Sergey Levine. Reinforcement learning and control as probabilistic inference: Tutorial and review. CoRR, abs/1805.00909, 2018.
[17] Finale Doshi-Velez and George Dimitri Konidaris. Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations. In *IJCAI*, 2016.
[18] Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocttäschel. A survey of zero-shot generalisation in deep reinforcement learning. J. Artif. Intell. Res., 76:201-264, 2023.
[19] Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson. VariBAD: A very good method for bayes-adaptive deep RL via meta-learning. In ICLR, 2020.
[20] Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, and Daniel M. Faissol. Single episode policy transfer in reinforcement learning. In ICLR, 2020.
[21] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017.
[22] Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, and Chelsea Finn. Offline meta-reinforcement learning with advantage weighting. In ICML, 2021.
[23] Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine. Reinforcement learning with deep energy-based policies. In ICML, 2017.
[24] Jing-Cheng Pang, Tian Xu, Shengyi Jiang, Yu-Ren Liu, and Yang Yu. Sparsity prior regularized q-learning for sparse action tasks. CoRR, abs/2105.08666, 2021.
[25] Scott Fujimoto, David Meger, and Doina Precup. Off-policy deep reinforcement learning without exploration. In ICML, 2019.
[26] Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. In NeurIPS, 2020.
[27] Yifan Wu, George Tucker, and Ofir Nachum. Behavior regularized offline reinforcement learning. CoRR, abs/1911.11361, 2019.
[28] Riashat Islam, Komal K. Teru, and Deepak Sharma. Off-policy policy gradient algorithms by constraining the state distribution shift. CoRR, abs/1911.06970, 2019.