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Massively Parallel Methods for Deep Reinforcement Learning

, , , , , , , , , , , , , and . (2015)cite arxiv:1507.04296Comment: Presented at the Deep Learning Workshop, International Conference on Machine Learning, Lille, France, 2015.

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Mastering the game of Go with deep neural networks and tree search, , , , , , , , , and . (2016)Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration., , and . ISER, volume 88 of Springer Tracts in Advanced Robotics, page 371-386. Springer, (2012)Authenticity, Relatability and Collaborative Approaches to Sharing Knowledge about Assistive Living Technology., , , , and . CSCW, page 82-94. ACM, (2015)Looking Backwards, Looking Forward: Cyberculture Studies 1990-2000. Web.Studies: Rewiring Media Studies for the Digital Age, Arnold, London, (2000)Non-Linear Monte-Carlo Search in Civilization II., , and . IJCAI, page 2404-2410. IJCAI/AAAI, (2011)Scene understanding for a high-mobility walking robot., , , , , , and . IROS, page 1144-1151. IEEE, (2015)Prioritized Experience Replay, , , and . (2015)cite arxiv:1511.05952Comment: Published at ICLR 2016.Expected Eligibility Traces., , , , , and . AAAI, page 9997-10005. AAAI Press, (2021)Natural Value Approximators: Learning when to Trust Past Estimates., , , , , and . NIPS, page 2120-2128. (2017)Discovering Reinforcement Learning Algorithms., , , , , , and . NeurIPS, (2020)