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Successor Features for Transfer in Reinforcement Learning., , , and . CoRR, (2016)Successor Features for Transfer in Reinforcement Learning., , , , , , and . NIPS, page 4055-4065. (2017)Risk-Aware Transfer in Reinforcement Learning using Successor Features., , , and . NeurIPS, page 17298-17310. (2021)The Phenomenon of Policy Churn., , , and . NeurIPS, (2022)The Value Equivalence Principle for Model-Based Reinforcement Learning., , , and . NeurIPS, (2020)Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement., , , , , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 510-519. PMLR, (2018)System Analysis of State-Aware Resource Allocation for Closed-Loop Control Systems., , , , , and . GLOBECOM, page 1-6. IEEE, (2020)On Efficiency in Hierarchical Reinforcement Learning., , , , , and . NeurIPS, (2020)Approximate Value Equivalence., , and . NeurIPS, (2022)The Predictron: End-To-End Learning and Planning., , , , , , , , , and 1 other author(s). ICML, volume 70 of Proceedings of Machine Learning Research, page 3191-3199. PMLR, (2017)