Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
conference on learning theory, 2019.
We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes. We design new algorithms for RL with a generic model class and analyze their statistical properties. Our algorithms have sample complexity governed by a new structural parameter called the witness rank, which we s...More
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