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个人简介
My research focuses on problems that are important to a general, reinforcement-learning-based intelligent system, but are not well understood yet. Such problems that I studied previously include 1) developing improved algorithms and theories for the average-reward formulation of reinforcement learning (RL), 2) designing algorithms that plan with a learned model of the world to achieve higher data efficiency, and 3) discovering temporal abstraction to achieve faster planning.
研究兴趣
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PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (2019): 3649-3655
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