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Imagination-Augmented Agents for Deep Reinforcement Learning., , , , , , , , , and 5 other author(s). NIPS, page 5690-5701. (2017)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)Mastering the game of Go with deep neural networks and tree search, , , , , , , , , and 1 other author(s). Nature, 529 (7587): 484--489 (2016)Mastering the game of Go without human knowledge., , , , , , , , , and 7 other author(s). Nat., 550 (7676): 354-359 (2017)Counterfactual Credit Assignment in Model-Free Reinforcement Learning., , , , , , , , , and 3 other author(s). CoRR, (2020)On the role of planning in model-based deep reinforcement learning., , , , , , , , , and . CoRR, (2020)The Predictron: End-To-End Learning and Planning., , , , , , , , , and 1 other author(s). CoRR, (2016)Augmenting learning using symmetry in a biologically-inspired domain., , , , and . CoRR, (2019)