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The Value-Improvement Path: Towards Better Representations for Reinforcement Learning.

, , , , , , и . AAAI, стр. 7160-7168. AAAI Press, (2021)

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André Noll Barreto

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Laplacian using Abstract State Transition Graphs: A Framework for Skill Acquisition., , и . BRACIS, стр. 263-268. IEEE, (2019)Value-Aware Loss Function for Model-based Reinforcement Learning., , и . AISTATS, том 54 из Proceedings of Machine Learning Research, стр. 1486-1494. PMLR, (2017)The Predictron: End-To-End Learning and Planning., , , , , , , , , и 1 other автор(ы). ICML, том 70 из Proceedings of Machine Learning Research, стр. 3191-3199. PMLR, (2017)System Analysis of State-Aware Resource Allocation for Closed-Loop Control Systems., , , , , и . GLOBECOM, стр. 1-6. IEEE, (2020)Approximate Value Equivalence., , и . NeurIPS, (2022)On Efficiency in Hierarchical Reinforcement Learning., , , , , и . NeurIPS, (2020)Universal Successor Features Approximators., , , , , , , и . ICLR (Poster), OpenReview.net, (2019)Position: Video as the New Language for Real-World Decision Making., , , , , , , и . ICML, OpenReview.net, (2024)Successor Features for Transfer in Reinforcement Learning., , , и . CoRR, (2016)Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement., , , , , , , , и . ICML, том 80 из Proceedings of Machine Learning Research, стр. 510-519. PMLR, (2018)