Roberta Raileanu and Rob Fergus. Decoupling value and policy for generalization in reinforcement learning. In International Conference on Machine Learning, pages 8787-8798. PMLR, 2021.
Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, and Rob Fergus. Automatic data augmentation for generalization in reinforcement learning. Advances in Neural Information Processing Systems, 34:5402-5415, 2021.
Or Rivlin, Tamir Hazan, and Erez Karpas. Generalized planning with deep reinforcement learning. arXiv preprint arXiv:2005.02305, 2020.
Noga H Rotman, Michael Schapira, and Aviv Tamar. Online safety assurance for learning-augmented systems. In Proceedings of the 19th ACM Workshop on Hot Topics in Networks, pages 88-95, 2020.
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.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, and Kimin Lee. State entropy maximization with random encoders for efficient exploration. In International Conference on Machine Learning, pages 9443-9454. PMLR, 2021.
Shai Shalev-Shwartz and Shai Ben-David. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
Harshinder Singh, Neeraj Misra, Vladimir Hnizdo, Adam Fedorowicz, and Eugene Demchuk. Nearest neighbor estimates of entropy. American journal of mathematical and management sciences, 23 (3-4):301-321, 2003.
Anoopkumar Sonar, Vincent Pacelli, and Anirudha Majumdar. Invariant policy optimization: Towards stronger generalization in reinforcement learning. In Learning for Dynamics and Control, pages 21-33. PMLR, 2021.
Adam Stooke, Kimin Lee, Pieter Abbeel, and Michael Laskin. Decoupling representation learning from reinforcement learning. In International Conference on Machine Learning, pages 9870-9879. PMLR, 2021.
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel. Value iteration networks. Advances in neural information processing systems, 29, 2016.
Aviv Tamar, Daniel Soudry, and Ev Zisselman. Regularization guarantees generalization in bayesian reinforcement learning through algorithmic stability. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 8423-8431, 2022.
Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, and Lexing Xie. Action schema networks: Generalised policies with deep learning. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michael Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al. Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782):350-354, 2019.
Marin Vlastelica, Michal Rolínek, and Georg Martius. Neuro-algorithmic policies enable fast combinatorial generalization. arXiv preprint arXiv:2102.07456, 2021.
Kaixin Wang, Bingyi Kang, Jie Shao, and Jiashi Feng. Improving generalization in reinforcement learning with mixture regularization. Advances in Neural Information Processing Systems, 33: 7968-7978, 2020.
Peter R Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, et al. Outracing champion gran turismo drivers with deep reinforcement learning. Nature, 602(7896): 223-228, 2022.