[28] J. Fan and W. Li. Dribo: Robust deep reinforcement learning via multi-view information bottleneck. In International Conference on Machine Learning, pages 6074-6102. PMLR, 2022.
[29] J. Farebrother, M. C. Machado, and M. H. Bowling. Generalization and regularization in dqn. ArXiv, abs/1810.00123, 2018.
[30] Y. Flet-Berliac, J. Ferret, O. Pietquin, P. Preux, and M. Geist. Adversarily guided actor-critic. CoRR, abs/2102.04376, 2021. URL https://arxiv.org/abs/2102.04376.
[31] S. Fort, H. Hu, and B. Lakshminarayanan. Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757, 2019.
[32] M. Fortunato, M. G. Azar, B. Piot, J. Menick, I. Osband, A. Graves, V. Mnih, R. Munos, D. Hassabis, O. Pietquin, et al. Noisy networks for exploration. arXiv preprint arXiv:1706.10295, 2017.
[33] R. Fruit and A. Lazaric. Exploration-exploitation in mdps with options. In Artificial intelligence and statistics, pages 576-584. PMLR, 2017.
[34] X. Fu, G. Yang, P. Agrawal, and T. Jaakkola. Learning task informed abstractions. In International Conference on Machine Learning, pages 3480-3491. PMLR, 2021.
[35] D. Ghosh, J. Rahme, A. Kumar, A. Zhang, R. P. Adams, and S. Levine. Why generalization in rl is difficult: Epistemic pomdpds and implicit partial observability. Advances in Neural Information Processing Systems, 34:25502-25515, 2021.
[36] J. Grigsby and Y. Qi. Measuring visual generalization in continuous control from pixels. ArXiv, abs/2010.06740, 2020.
[37] D. Hafner. Benchmarking the spectrum of agent capabilities. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=1W0z96MFEoH.
[38] D. Hafner, T. P. Lillicrap, M. Norouzi, and J. Ba. Mastering atari with discrete world models. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=0oabwyZb0u.
[39] A. Hallak, D. Di Castro, and S. Mannor. Contextual markov decision processes. arXiv preprint arXiv:1502.02259, 2015.
[40] N. Hansen and X. Wang. Generalization in reinforcement learning by soft data augmentation. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13611-13617. IEEE, 2021.
[41] N. Hansen, H. Su, and X. Wang. Stabilizing deep q-learning with convnets and vision transformers under data augmentation. Advances in Neural Information Processing Systems, 34:3680-3693, 2021.
[42] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[43] M. Hessel, J. Modayil, H. Van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver. Rainbow: Combining improvements in deep reinforcement learning. In Thirty-second AAAI conference on artificial intelligence, 2018.
[44] D. Horgan, J. Quan, D. Budden, G. Barth-Maron, M. Hessel, H. van Hasselt, and D. Silver. Distributed prioritized experience replay. In International Conference on Learning Representations, 2018. URL https://openreview.net/forum?id=H1Dy---OZ.
[45] M. Igl, K. Ciosek, Y. Li, S. Tschiatschek, C. Zhang, S. Devlin, and K. Hofmann. Generalization in reinforcement learning with selective noise injection and information bottleneck. In Advances in Neural Information Processing Systems, pages 13956-13968, 2019.