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The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning

, , , , , and . ICLR, (2017)cite arxiv:1704.04651.

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Fast LSTD Using Stochastic Approximation: Finite Time Analysis and Application to Traffic Control., , and . ECML/PKDD (2), volume 8725 of Lecture Notes in Computer Science, page 66-81. Springer, (2014)Combining policy gradient and Q-learning., , , and . ICLR (Poster), OpenReview.net, (2017)Sample Efficient Actor-Critic with Experience Replay., , , , , , and . ICLR (Poster), OpenReview.net, (2017)Distributional Reinforcement Learning With Quantile Regression., , , and . AAAI, page 2892-2901. AAAI Press, (2018)Leverage the Average: an Analysis of Regularization in RL., , , , , and . CoRR, (2020)Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint., , , , , , , and . AAMAS, page 489-497. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Counterfactual Credit Assignment in Model-Free Reinforcement Learning., , , , , , , , , and 4 other author(s). ICML, volume 139 of Proceedings of Machine Learning Research, page 7654-7664. PMLR, (2021)Understanding Self-Predictive Learning for Reinforcement Learning., , , , , , , , , and 6 other author(s). ICML, volume 202 of Proceedings of Machine Learning Research, page 33632-33656. PMLR, (2023)VA-learning as a more efficient alternative to Q-learning., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 33739-33757. PMLR, (2023)The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 29210-29231. PMLR, (2023)