[10] Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. Rainbow: Combining Improvements in Deep Reinforcement Learning, October 2017. URL http://arxiv.org/abs/1710.02298. arXiv:1710.02298 [cs].
[11] Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. Nature, 588(7839):604-609, December 2020. ISSN 0028-0836, 1476-4687. doi: 10. 1038/s41586-020-03051-4. URL http://arxiv.org/abs/1911.08265.arXiv:1911.08265 [cs, stat].
[12] Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdelmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, and Martin Riedmiller. DeepMind Control Suite, January 2018. URL http://arxiv.org/abs/1801.00690. arXiv:1801.00690 [cs].
[13] Emanuel Todorov, Tom Erez, and Yuval Tassa. MuJoCo: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026-5033, October 2012. doi: 10.1109/IROS.2012.6386109. ISSN: 2153-0866.
[14] Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal. Minimalistic Gridworld Environment for OpenAI Gym, 2018. URL https://github.com/maximecb/gym-minigrid. Publication Title: GitHub repository.
[15] Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, and Kimin Lee. State Entropy Maximization with Random Encoders for Efficient Exploration. In Proceedings of the 38th International Conference on Machine Learning, pages 9443-9454. PMLR, July 2021. URL https://proceedings.mlr.press/v139/seo21a.html. ISSN: 2640-3498.
[16] Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine. Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. In Advances in Neural Information Processing Systems, volume 33, pages 13049-13061. Curran Associates, Inc., 2020. URL https://proceedings.neurips.cc/paper/2020/bitstream/985e9a46e10005356bbaf194249f6856-AAbstract.html.
[17] Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Sahara, Thien Huu Nguyen, and Yoshua Bengio. BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning, December 2019. URL http://arxiv.org/abs/1810.08272. arXiv:1810.08272 [cs].
[18] Sham Machandranath Kakade. On the sample complexity of reinforcement learning. PhD thesis, University of London, University College London (United Kingdom), 2003.
[19] Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E. Schapire. Contextual Decision Processes with low Bellman rank are PAC-Learnable. In Proceedings of the 34th International Conference on Machine Learning, pages 1704-1713. PMLR, July 2017. URL https://proceedings.mlr.press/v70/jiang17c.html. ISSN: 2640-3498.
[20] Chi Jin, Zhuoran Yang, Zhaoran Wang, and Michael I. Jordan. Provably Efficient Reinforcement Learning with Linear Function Approximation. arXiv:1907.05388 [cs, math, stat], August 2019. URL http://arxiv.org/abs/1907.05388. arXiv:1907.05388.
[21] Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, and Ruosong Wang. Bilinear Classes: A Structural Framework for Provable Generalization in RL. In Proceedings of the 38th International Conference on Machine Learning, pages 2826-2836. PMLR, July 2021. URL https://proceedings.mlr.press/v139/du21a.html. ISSN: 2640-3498.
[22] Chi Jin, Qinghua Liu, and Sobhan Miryoosefi. Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms. In Advances in Neural Information Processing Systems, volume 34, pages 13406-13418. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper/2021/bit/6f5e4e86a87220e5d361ad82f1ebc335-AAbstract.html.