[28] Laurent Orseau, Levi Lelis, Tor Lattimore, and Theophane Weber. Single-Agent Policy Tree Search with Guarantees. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 3205-3215, 2018. URL https://proceedings.neurips.cc/paper/2018/bit/52c5189391854c93e8a0e1326e56c14f-Abstract.html.
[29] Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, and Oriol Vinyals. Vector Quantized Models for Planning. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8302-8313. PMLR, 2021. URL http://proceedings.mlr.press/v139/ozair21a.html.
[30] Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B Hamrick, Nicolas Heess, Alexander Neitz, and Theophane Weber. Divide-and-Conquer Monte Carlo Tree Search for Goal-Directed Planning. ArXiv preprint, abs/2004.11410, 2020. URL https://arxiv.org/abs/2004.11410.
[31] Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, and Sergey Levine. Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/bit/ c8d3a760ebab631565f8509d84b3b3f1-AAbstract.html.
[32] Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Kosta Derpanis, Joseph Lim, Kostas Daniilidis, and Andrew Jaegle. KeyIn: Keyframing for Visual Planning. Conference on Learning for Dynamics and Control, 2020.
[33] Stanislas Polu and Ilya Sutskever. Generative Language Modeling for Automated Theorem Proving. ArXiv preprint, abs/2009.03393, 2020. URL https://arxiv.org/abs/2009.03393.
[34] Reuven Y Rubinstein and Dirk P Kroese. The Cross-Entropy Method: a Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning, volume 133. Springer, 2004.
[35] Stuart J Russell. Artificial Intelligence: A Modern Approach. Pearson Education, Inc., 2010.
[36] Nikolay Savinov, Alexey Dosovitskiy, and Vladlen Koltun. Semi-Parametric Topological Memory for Navigation. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net, 2018. URL https://openreview.net/forum?id=SygwwGbRW.
[37] Max-Philipp B. Schrader. gym-sokoban. https://github.com/mpSchrader/gym-sokoban, 2018.
[38] Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. Nature, 588(7839):604-609, 2020.
[39] David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. A General Reinforcement Learning Algorithm that Masters Chess, Shogi, and Go Through Self-Play. Science, 362(6419):1140-1144, 2018.
[40] Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, and Sergey Levine. COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning. ArXiv preprint, abs/2010.14500, 2020. URL https://arxiv.org/abs/2010.14500.