[20] Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio, and Sergey Levine. Infobot: Transfer and exploration via the information bottleneck. arXiv preprint arXiv:1901.10902, 2019.
[21] Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, and Katja Hofmann. Generalization in reinforcement learning with selective noise injection and information bottleneck. Advances in neural information processing systems, 32, 2019.
[22] Xingyu Lu, Kimin Lee, Pieter Abbeel, and Stas Tiomkin. Dynamics generalization via information bottleneck in deep reinforcement learning. arXiv preprint arXiv:2008.00614, 2020.
[23] Glen Berseth, Daniel Geng, Coline Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, and Sergey Levine. Smirl: Surprise minimizing reinforcement learning in unstable environments. arXiv preprint arXiv:1912.05510, 2019.
[24] Joshua Achiam and Shankar Sastry. Surprise-based intrinsic motivation for deep reinforcement learning. arXiv preprint arXiv:1703.01732, 2017.
[25] Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A Efros. Large-scale study of curiosity-driven learning. arXiv preprint arXiv:1808.04355, 2018.
[26] Lucy Lai and Samuel J Gershman. Policy compression: An information bottleneck in action selection. In Psychology of Learning and Motivation, volume 74, pages 195-232. Elsevier, 2021.
[27] Shuchen Wu, Noémi Élteto, Ishita Dasgupta, and Eric Schulz. Learning structure from the ground up—hierarchical representation learning by chunking. Advances in Neural Information Processing Systems, 35:36706–36721, 2022.
[28] Noémi Éltető, Dezso Nemeth, Karolina Janacsek, and Peter Dayan. Tracking human skill learning with a hierarchical bayesian sequence model. PLoS Computational Biology, 18(11): e1009866, 2022.
[29] Marcel Binz and Eric Schulz. Modeling human exploration through resource-rational reinforcement learning. In Advances in Neural Information Processing Systems, 2022.
[30] Sreejan Kumar, Carlos G Correa, Ishita Dasgupta, Raja Marjieh, Michael Y Hu, Robert Hawkins, Jonathan D Cohen, Karthik Narasimhan, Tom Griffiths, et al. Using natural language and program abstractions to instill human inductive biases in machines. Advances in Neural Information Processing Systems, 35:167-180, 2022.
[31] Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision transformer: Reinforcement learning via sequence modeling. Advances in neural information processing systems, 34:15084-15097, 2021.
[32] Khalid Sayood. Introduction to data compression. Morgan Kaufmann, 2017.
[33] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
[34] Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdelmaleki, Josh Merel, Andrew Lefrancq, et al. Deepmind control suite. arXiv preprint arXiv:1801.00690, 2018.
[35] Arjun Sharma and Kris Kitani. Phase-parametric policies for reinforcement learning in cyclic environments. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
[36] Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus. Improving sample efficiency in model-free reinforcement learning from images. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 10674-10681, 2021.