[19] D. Hafner, T. P. Lillicrap, J. Ba, and M. Norouzi. Dream to control: Learning behaviors by latent imagination. In International Conference on Learning Representations, (ICLR), 2020.
[20] J. Humplik, A. Galashov, L. Hasenclever, P. A. Ortega, Y. W. Teh, and N. Heess. Meta reinforcement learning as task inference. arXiv preprint arXiv: 1905.06424, 2019.
[21] 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 Neural Information Processing Systems (NeurIPS), pages 32:13978-13990, 2019.
[22] A. Jabri, K. Hsu, B. Eysenbach, A. Gupta, S. Levine, and C. Finn. Unsupervised curricula for visual meta-reinforcement learning. In Neural Information Processing Systems (NeurIPS), volume 32, 2019.
[23] E. Jang, S. Gu, and B. Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations (ICLR), 2017.
[24] J. Kaddour, S. Sæmundsson, and M. P. Deisenroth. Probabilistic active meta-learning. In Neural Information Processing Systems (NeurIPS), pages 33:20813-20822, 2020.
[25] L. P. Kaelbling, M. L.Littman, and A. R.Cassandra. Acting optimally in partially observable stochastic domains. In National Conference on Artificial Intelligence, 1994.
[26] L. P. Kaelbling, M. L. Littman, and A. R. Cassandra. Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101:99-134, 1998.
[27] K. Khetarpal, M. Riemer, I. Rish, and D. Precup. Towards continual reinforcement learning: A review and perspectives. In Journal of Artificial Intelligence Research, pages 1401-1476, 2022.
[28] D. P. Kingma and M. Welling. Auto-encoding variational bayes. In International Conference on Learning Representations (ICLR), 2014.
[29] D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling. Semi-supervised learning with deep generative models. In Neural Information Processing Systems (NeurIPS), 2014.
[30] L. Kirsch, S. van Steenkiste, and J. Schmidhuber. Improving generalization in meta reinforcement learning using learned objectives. In International Conference on Learning Representations (ICLR), 2020.
[31] M. Laskin, K. Lee, A. Stooke, L. Pinto, P. Abbeel, and A. Srinivas. Reinforcement learning with augmented data. In Neural Information Processing Systems (NeurIPS), pages 33:19884-19895, 2020.
[32] A. X. Lee, A. Nagabandi, P. Abbeel, and S. Levine. Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model. In Neural Information Processing Systems (NeurIPS), pages 33:741-752, 2020.
[33] K. Lee, K. Lee, J. Shin, and H. Lee. Network randomization: A simple technique for generalization in deep reinforcement learning. In International Conference on Learning Representations (ICLR), 2020.
[34] K. Lee, Y. Seo, S. Lee, H. Lee, and J. Shin. Context-aware dynamics model for generalization in model-based reinforcement learning. In International Conference on Machine Learning (ICML), pages 119:5757-5766, 2020.
[35] S. Lee and S. Chung. Improving generalization in meta-RL with imaginary tasks from latent dynamics mixture. In Neural Information Processing Systems (NeurIPS), pages 34:27222-27235, 2021.
[36] Z. Lin, G. Thomas, G. Yang, and T. Ma. Model-based adversarial meta-reinforcement learning. In Neural Information Processing Systems (NeurIPS), pages 33:10161-10173, 2020.
[37] S. Liu, A. J. Davison, and E. Johns. Self-supervised generalisation with meta auxiliary learning. In Neural Information Processing Systems (NeurIPS), pages 32:1679-1689, 2019.