[37] Richard Bellman, Irving Glicksberg, and Oliver Gross. On the "bang-bang" control problem. Quarterly of Applied Mathematics, 14(1):11-18, 1956.
[38] Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier, and Daniela Rus. Is bang-bang control all you need? solving continuous control with bernoulli policies. Advances in Neural Information Processing Systems, 34: 27209-27221, 2021.
[39] Benjamin Eysenbach and Sergey Levine. Maximum entropy rl (provably) solves some robust rl problems. arXiv preprint arXiv:2103.06257, 2021.
[40] Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning. In Conference on robot learning, pages 1094–1100. PMLR, 2020.
[41] Michael Laskin, Aravind Srinivas, and Pieter Abbeel. Curl: Contrastive unsupervised representations for reinforcement learning. In International Conference on Machine Learning, pages 5639-5650. PMLR, 2020.
[42] Alex Graves. Practical variational inference for neural networks. Advances in neural information processing systems, 24, 2011.
[43] Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47:253-279, 2013.
[44] Jürgen Schmidhuber. Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE transactions on autonomous mental development, 2(3):230–247, 2010.
[45] Mona M Garvert, Tankred Saanum, Eric Schulz, Nicolas W Schuck, and Christian F Doeller. Hippocampal spatio-predictive cognitive maps adaptively guide reward generalization. Nature Neuroscience, 26(4):615-626, 2023.
[46] Can Demircan, Tankred Saanum, Leonardo Pettini, Marcel Binz, Blazej M Baczkowski, Paula Kaanders, Christian F Doeller, Mona M Garvert, and Eric Schulz. Language aligned visual representations predict human behavior in naturalistic learning tasks. arXiv preprint arXiv:2306.09377, 2023.
[47] Neil R Bramley, Peter Dayan, Thomas L Griffiths, and David A Lagnado. Formalizing neurath's ship: Approximate algorithms for online causal learning. *Psychological review*, 124(3):301, 2017.
[48] Bonan Zhao, Christopher G Lucas, and Neil R Bramley. How do people generalize causal relations over objects? a non-parametric bayesian account. Computational Brain & Behavior, 5 (1):22-44, 2022.
[49] Francesca Greenstreet, Hernando Martinez Vergara, Sthitapranjya Pati, Laura Schwarz, Matthew Wisdom, Fred Marbach, Yvonne Johansson, Lars Rollik, Theodore Moskovitz, Claudia Clopath, et al. Action prediction error: a value-free dopaminergic teaching signal that drives stable learning. BiorXiv, pages 2022-09, 2022.
[50] Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905, 2018.
[51] Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019.
[52] Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Icml, 2010.
[53] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.