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Abstract: The majority reinforcement learning (RL) algorithms are largely categorized as model-free and model-based through whether a simulation model is used in the algorithm. However, both of these two categories have their own issues, especially incorporating with function approximation: the exploration with arbitrary function approximation in model-free RL algorithms is difficult, while optimal planning becomes intractable in model-based RL algorithms with neural simulators. In this talk, I will present our recent work on exploiting the power of representation in RL to bypass these difficulties. Specifically, we designed practical algorithms for extracting useful representations, with the goal of improving statistical and computational efficiency in exploration vs. exploitation tradeoff and empirical performance in RL. We provide rigorous theoretical analysis of our algorithm, and demonstrate the practical superior performance over the existing state-of-the-art empirical algorithms on several benchmarks. 

Bio: Bo Dai is an assistant professor in Georgia Tech and a staff research scientist in Google DeepMind. He obtained his Ph.D. from Georgia Tech. His research interest lies in developing principled and practical machine learning methods for Decision AI, including reinforcement learning. He is the recipient of the best paper award of AISTATS and NeurIPS workshop. He regularly serves as area chair or senior program committee member at major AI/ML conferences such as ICML, NeurIPS, AISTATS, and ICLR.

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