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The Predictron: End-To-End Learning and Planning., , , , , , , , , and 1 other author(s). CoRR, (2016)Physically Embedded Planning Problems: New Challenges for Reinforcement Learning., , , , , , , , , and 2 other author(s). CoRR, (2020)Augmenting learning using symmetry in a biologically-inspired domain., , , , and . CoRR, (2019)Bayes-Adaptive Simulation-based Search with Value Function Approximation., , , and . NIPS, page 451-459. (2014)Retrieval-Augmented Reinforcement Learning., , , , , , , , , and 5 other author(s). ICML, volume 162 of Proceedings of Machine Learning Research, page 7740-7765. PMLR, (2022)Muesli: Combining Improvements in Policy Optimization., , , , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 4214-4226. PMLR, (2021)Mastering the game of Go with deep neural networks and tree search, , , , , , , , , and 1 other author(s). Nature, 529 (7587): 484--489 (2016)Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search., , and . J. Artif. Intell. Res., (2013)Increasing the Action Gap: New Operators for Reinforcement Learning., , , , and . CoRR, (2015)Learning functions across many orders of magnitudes., , , and . CoRR, (2016)