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Temporal Abstraction in Reinforcement Learning with the Successor Representation.

, , , and . J. Mach. Learn. Res., (2023)

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Deep Laplacian-based Options for Temporally-Extended Exploration., and . ICML, volume 202 of Proceedings of Machine Learning Research, page 17198-17217. PMLR, (2023)State of the Art Control of Atari Games Using Shallow Reinforcement Learning., , , and . CoRR, (2015)Agent-State Construction with Auxiliary Inputs., , and . Trans. Mach. Learn. Res., (2023)Recurrent Linear Transformers., , , and . CoRR, (2023)Introspective Agents: Confidence Measures for General Value Functions., , , and . AGI, volume 9782 of Lecture Notes in Computer Science, page 258-261. Springer, (2016)Eigenoption Discovery through the Deep Successor Representation., , , , , and . ICLR (Poster), OpenReview.net, (2018)Exploration in Reinforcement Learning with Deep Covering Options., , , and . ICLR, OpenReview.net, (2020)Demystifying the Recency Heuristic in Temporal-Difference Learning., , and . CoRR, (2024)Temporal Abstraction in Reinforcement Learning with the Successor Representation., , , and . J. Mach. Learn. Res., (2023)Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint)., , , , , , and . AAAI, page 22713. AAAI Press, (2024)