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Rainbow: Combining Improvements in Deep Reinforcement Learning

, , , , , , , , , and . (2017)cite arxiv:1710.02298Comment: Under review as a conference paper at AAAI 2018.

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The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning, , , , , and . ICLR, (2017)cite arxiv:1704.04651.Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards., , , , , , , , , and . CoRR, (2017)Rainbow: Combining Improvements in Deep Reinforcement Learning, , , , , , , , , and . (2017)cite arxiv:1710.02298Comment: Under review as a conference paper at AAAI 2018.Learning from Demonstrations for Real World Reinforcement Learning., , , , , , , , , and 2 other author(s). CoRR, (2017)Boosted and reward-regularized classification for apprenticeship learning., , and . AAMAS, page 1249-1256. IFAAMAS/ACM, (2014)Rainbow: Combining Improvements in Deep Reinforcement Learning., , , , , , , , , and . AAAI, page 3215-3222. AAAI Press, (2018)Geometric Entropic Exploration., , , , , , , , , and . CoRR, (2021)Neural Predictive Belief Representations., , , , , and . CoRR, (2018)BYOL works even without batch statistics., , , , , , , , , and 1 other author(s). CoRR, (2020)Nash Learning from Human Feedback., , , , , , , , , and 7 other author(s). CoRR, (2023)