Georgia Institute of Technology College of Sciences

How to achieve the optimal control for general stochastic nonlinear is notoriously difficult, which becomes even more difficult by involving learning and exploration for unknown dynamics in reinforcement learning setting. 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.