[84] C. Packer, K. Gao, J. Kos, P. Krahenbuhl, V. Koltun, and D. X. Song. Assessing generalization in deep reinforcement learning. *ArXiv*, abs/1810.12282, 2018.
[85] J. Parker-Holder, M. Jiang, M. Dennis, M. Samvelyan, J. Foerster, E. Grefenstette, and T. Rocktäschel. Evolving curricula with regret-based environment design. arXiv preprint arXiv:2203.01302, 2022.
[86] D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning, pages 2778-2787. PMLR, 2017.
[87] T. Pearce, F. Leibfried, and A. Brintrup. Uncertainty in neural networks: Approximately bayesian ensembling. In International conference on artificial intelligence and statistics, pages 234–244. PMLR, 2020.
[88] V. H. Pong, M. Dalal, S. Lin, A. Nair, S. Bahl, and S. Levine. Skew-fit: State-covering self-supervised reinforcement learning. arXiv preprint arXiv:1903.03698, 2019.
[89] M. M. Rahman and Y. Xue. Adversarial style transfer for robust policy optimization in reinforcement learning. 2021.
[90] R. Raileanu and R. Fergus. Decoupling value and policy for generalization in reinforcement learning. In International Conference on Machine Learning, pages 8787-8798. PMLR, 2021.
[91] R. Raileanu and T. Rocktäschel. Ride: Rewarding impact-driven exploration for procedurally-generated environments. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=rkg-TJBFPB.
[92] R. Raileanu, M. Goldstein, D. Yarats, I. Kostrikov, and R. Fergus. Automatic data augmentation for generalization in deep reinforcement learning. ArXiv, abs/2006.12862, 2020.
[93] A. Rajeswaran, K. Lowrey, E. Todorov, and S. M. Kakade. Towards generalization and simplicity in continuous control. ArXiv, abs/1703.02660, 2017.
[94] S. Ross, G. Gordon, and D. Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 627-635. JMLR Workshop and Conference Proceedings, 2011.
[95] J. Roy and G. Konidaris. Visual transfer for reinforcement learning via Wasserstein domain confusion. arXiv preprint arXiv:2006.03465, 2020.
[96] T. Schaul, J. Quan, I. Antonoglou, and D. Silver. Prioritized experience replay. arXiv preprint arXiv:1511.05952, 2015.
[97] J. Schmidhuber. A possibility for implementing curiosity and boredom in model-building neural controllers. In Proc. of the international conference on simulation of adaptive behavior: From animals to animals, pages 222-227, 1991.
[98] B. Settles. Active learning literature survey. 2009.
[99] A. Sonar, V. Pacelli, and A. Majumdar. Invariant policy optimization: Towards stronger generalization in reinforcement learning. ArXiv, abs/2006.01096, 2020.
[100] X. Song, Y. Jiang, S. Tu, Y. Du, and B. Neyshabur. Observational overfitting in reinforcement learning. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=HJli2hNKDH.
[101] A. Stanic, Y. Tang, D. Ha, and J. Schmidhuber. An investigation into the open world survival game drafter. In Decision Awareness in Reinforcement Learning Workshop at ICML 2022, 2022. URL https://openreview.net/forum?id=C5q00n9dBxC.