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• W3034974382 abstract "Decision-making agents with planning capabilities have achieved huge success in the challenging domain like Chess, Shogi, and Go. In an effort to generalize the planning ability to the more general tasks where the environment dynamics are not available to the agent, researchers proposed the MuZero algorithm that can learn the dynamical model through the interactions with the environment. In this paper, we provide a way and the necessary theoretical results to extend the MuZero algorithm to more generalized environments with continuous action space. Through numerical results on two relatively low-dimensional MuJoCo environments, we show the proposed algorithm outperforms the soft actor-critic (SAC) algorithm, a state-of-the-art model-free deep reinforcement learning algorithm." @default.
• W3034974382 created "2020-06-19" @default.
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• W3034974382 date "2020-06-12" @default.
• W3034974382 modified "2023-10-16" @default.
• W3034974382 title "Continuous Control for Searching and Planning with a Learned Model" @default.
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• W3034974382 doi "https://doi.org/10.48550/arxiv.2006.07430" @default.
• W3034974382 hasPublicationYear "2020" @default.
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