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Late-Breaking Papers: Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
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  • title: Late-Breaking Papers: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
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            Late-Breaking Papers: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
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            Late-Breaking Papers: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

            14. prosince 2019

            Řečníci

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            Julian Schrittwieser

            Sprecher:in · 1 Follower:in

            O prezentaci

            In recent years, the use of deep neural networks as function approximators has enabled researchers to extend reinforcement learning techniques to solve increasingly complex control tasks. The emerging field of deep reinforcement learning has led to remarkable empirical results in rich and varied domains like robotics, strategy games, and multiagent interaction. This workshop will bring together researchers working at the intersection of deep learning and reinforcement learning, and it will help…

            Organizátor

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            NIPS 2019

            Konto · 961 Follower:innen

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            KI und Datenwissenschaft

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            O organizátorovi (NIPS 2019)

            Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

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