[38] J. J. Martin. Bayesian Decision Problems and Markov Chains. Wiley, 1967.
[39] B. Mehta, T. Deleu, S. C. Raparthy, C. J. Pal, and L. Paull. Curriculum in gradient-based meta-reinforcement learning. arXiv preprint arXiv:2002.07956, 2020.
[40] L. C. Melo. Transformers are meta-reinforcement learners. In International Conference on Machine Learning (ICML), pages 162:15340-15359, 2022.
[41] R. Mendonca, X. Geng, C. Finn, and S. Levine. Meta-reinforcement learning robust to distributional shift via model identification and experience relabeling. arXiv preprint arXiv: 2006.07178, 2020.
[42] T. Nam, S.-H. Sun, K. Pertsch, S. J. Hwang, and J. J. Lim. Skill-based meta-reinforcement learning. In International Conference on Learning Representations (ICLR), 2022.
[43] R. Raileanu, M. Goldstein, A. Szlam, and R. Fergus. Fast adaptation to new environments via policy-dynamics value functions. In International Conference on Machine Learning (ICML), pages 119:7920-7931, 2020.
[44] R. Raileanu, M. Goldstein, D. Yarats, I. Kostrikov, and R. Fergus. Automatic data augmentation for generalization in reinforcement learning. In Neural Information Processing Systems (NeurIPS), pages 34:5402-5415, 2021.
[45] A. Rajeswaran, K. Lowrey, E. Todorov, and S. M. Kakade. Towards generalization and simplicity in continuous control. In Neural Information Processing Systems (NeurIPS), pages 30:6550-6561, 2017.
[46] K. Rakelly, A. Zhou, C. Finn, S. Levine, and D. Quillen. Efficient off-policy meta-reinforcement learning via probabilistic context variables. In International Conference on Machine Learning (ICML), pages 97:5331-5340, 2019.
[47] D. Rao, F. Sadeghi, L. Hasenclever, M. Wulfmeier, M. Zambelli, G. Vezzani, D. Tirumala, Y. Aytar, J. Merel, N. Heess, and R. Hadsell. Learning transferable motor skills with hierarchical latent mixture policies. In International Conference on Learning Representations (ICLR), 2022.
[48] J. Rothfuss, D. Lee, I. Clavera, T. Asfour, and P. Abbeel. Prompt: Proximal meta-policy search. In International Conference on Learning Representations (ICLR), 2019.
[49] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv: 1707.06347, 2017.
[50] D. Shah, P. Xu, Y. Lu, T. Xiao, A. Toshev, S. Levine, and B. Ichter. Value function spaces: Skill-centric state abstractions for long-horizon reasoning. In International Conference on Learning Representations (ICLR), 2022.
[51] L. X. Shi, J. J. Lim, and Y. Lee. Skill-based model-based reinforcement learning. In Conference on Robot Learning, (CoRL), 2022.
[52] X. Song, Y. Jiang, S. Tu, Y. Du, and B. Neyshabur. Observational overfitting in reinforcement learning. In International Conference on Learning Representations (ICLR), 2020.
[53] Y. Song, A. Mavalankar, W. Sun, and S. Gao. Provably efficient model-based policy adaptation. In International Conference on Machine Learning (ICML), pages 119:9088-9098, 2020.
[54] B. C. Stadie, G. Yang, R. Houthooft, X. Chen, Y. Duan, Y. Wu, P. Abbeel, and I. Sutskever. The importance of sampling in meta-reinforcement learning. In Neural Information Processing Systems (NeurIPS), pages 31:9280-9290, 2018.
[55] L. Sun, H. Zhang, W. Xu, and M. Tomizuka. Paco: Parameter-compositional multi-task reinforcement learning. In Neural Information Processing Systems (NeurIPS), 2022.
[56] Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Sharing clusters among related groups: Hierarchical dirichlet processes. In Neural Information Processing Systems (NeurIPS), 2004.