[15] Assaf Hallak, Dotan Di Castro, and Shie Mannor. Contextual markov decision processes. CoRR, abs/1502.02259, 2015. URL http://arxiv.org/abs/1502.02259.
[16] Philip J. Ball, Cong Lu, Jack Parker-Holder, and Stephen J. Roberts. Augmented world models facilitate zero-shot dynamics generalization from a single offline environment. In Proceedings of the 38th International Conference on Machine Learning, pages 619-629. PMLR, 2021. URL http://proceedings.mlr.press/v139/ball21a.html.
[17] Shagun Sodhani, Amy Zhang, and Joelle Pineau. Multi-task reinforcement learning with context-based representations. In Proceedings of the 38th International Conference on Machine Learning, volume 139, pages 9767-9779. PMLR, 2021. URL http://proceedings.mlr.press/v139/sodhani21a.html.
[18] Shagun Sodhani, Franziska Meier, Joelle Pineau, and Amy Zhang. Block contextual MDPs for continual learning. In Learning for Dynamics and Control Conference, L4DC 2022, 23-24 June 2022, Stanford University, Stanford, CA, USA, pages 608-623. PMLR, 2022. URL https://proceedings.mlr.press/v168/sodhani22a.html.
[19] Richard Bellman. A markovian decision process. Indiana Univ. Math. J., 6:679-684, 1957. ISSN 0022-2518.
[20] Martin L Puterman and Eugene A Feinberg. Markov decision processes: discrete stochastic dynamic programming. SIAM review, 38(4):689, 1996.
[21] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
[22] Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world. In International Conference on Intelligent Robots and Systems, pages 23-30. IEEE, 2017. doi: 10.1109/IROS.2017.8202133. URL https://doi.org/10.1109/IROS.2017.8202133.
[23] Fereshteh Sadeghi and Sergey Levine. CAD2RL: real single-image flight without a single real image. In Robotics: Science and Systems XIII, 2017. doi: 10.15607/RSS.2017.XIII.034. URL http://www. roboticsproceedings.org/rss13/p34.html.
[24] Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel. Sim-to-real transfer of robotic control with dynamics randomization. In International Conference on Robotics and Automation, pages 1-8. IEEE, 2018. doi: 10.1109/ICRA.2018.8460528. URL https://doi.org/10.1109/ICRA.2018.8460528.
[25] Jan Matas, Stephen James, and Andrew J. Davison. Sim-to-real reinforcement learning for deformable object manipulation. In Conference on Robot Learning, pages 734-743. PMLR, 2018. URL http://proceedings.mlr.press/v87/matas18a.html.
[26] Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba. Learning dexterous in-hand manipulation. Int. J. Robotics Res., 39(1), 2020. doi: 10.1177/0278364919887447. URL https://doi.org/10.1177/0278364919887447.
[27] Alejandro Escontrela, George Yu, Peng Xu, Atil Iscen, and Jie Tan. Zero-shot terrain generalization for visual locomotion policies. CoRR, abs/2011.05513, 2020. URL https://arxiv.org/abs/2011.05513.
[28] Zac Wellmer and James T. Kwok. Dropout's dream land: Generalization from learned simulators to reality. In Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part I, volume 12975 of Lecture Notes in Computer Science, pages 255-270. Springer, 2021. doi: 10.1007/978-3-030-86486-6\_16. URL https://doi.org/10.1007/978-3-030-86486-6_16.
[29] Wenshuai Zhao, Jorge Pena Queralta, and Tomi Westerlund. Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In Symposium Series on Computational Intelligence, pages 737-744. IEEE, 2020. doi: 10.1109/SSCI47803.2020.9308468. URL https://doi.org/10.1109/SSCI47803.2020.9308468.
[30] Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, and Stephen J. Roberts. Same state, different task: Continual reinforcement learning without interference. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7143-7151. AAAI Press, 2022. URL https:// ojs.aaaai.org/index.php/AAAI/article/view/20674.