[45] Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J Pal, and Liam Paull. Active domain randomization. In Conference on Robot Learning, pages 1162-1176. PMLR, 2020.
[46] Rémi Munos and Csaba Szepesvári. Finite-time bounds for fitted value iteration. Journal of Machine Learning Research, 9(5), 2008.
[47] Fabio Muratore, Christian Eilers, Michael Gienger, and Jan Peters. Data-efficient domain randomization with bayesian optimization. IEEE Robotics and Automation Letters, 6(2):911-918, 2021.
[48] Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn. Learning to adapt in dynamic, real-world environments through meta-reinforcement learning. In International Conference on Learning Representations, 2018.
[49] 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. arXiv preprint arXiv:2206.13464, 2022.
[50] Georg Ostrovski, Pablo Samuel Castro, and Will Dabney. The difficulty of passive learning in deep reinforcement learning. Advances in Neural Information Processing Systems, 34:23283-23295, 2021.
[51] Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, and Soon-Jo Chung. Neural-fly enables rapid learning for agile flight in strong winds. Science Robotics, 7(66):eabm6597, 2022.
[52] Emilio Parisotto, Lei Jimmy Ba, and Ruslan Salakhutdinov. Actor-mimic: Deep multitask and transfer reinforcement learning. In ICLR (Poster), 2016.
[53] Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel. Sim-to-real transfer of robotic control with dynamics randomization. In 2018 IEEE international conference on robotics and automation (ICRA), pages 3803-3810. IEEE, 2018.
[54] Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi. Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach. In Machine Learning for Healthcare Conference, pages 147-163. PMLR, 2017.
[55] Roberta Raileanu, Max Goldstein, Arthur Szlam, and Rob Fergus. Fast adaptation to new environments via policy-dynamics value functions. In Proceedings of the 37th International Conference on Machine Learning, pages 7920-7931, 2020.
[56] Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, and Sergey Levine. Epopt: Learning robust neural network policies using model ensembles. arXiv preprint arXiv:1610.01283, 2016.
[57] Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen. Efficient off-policy meta-reinforcement learning via probabilistic context variables. In International conference on machine learning, pages 5331-5340. PMLR, 2019.
[58] Fabio Ramos, Rafael Carvalhaes Possas, and Dieter Fox. Bayessim: adaptive domain randomization via probabilistic inference for robotics simulators. arXiv preprint arXiv:1906.01728, 2019.
[59] Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al. Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588(7839):604-609, 2020.
[60] John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438, 2015.
[61] Jian Shen, Han Zhao, Weinan Zhang, and Yong Yu. Model-based policy optimization with unsupervised model adaptation. Advances in Neural Information Processing Systems, 33:2823-2834, 2020.