Information-Theoretic Considerations in Batch Reinforcement Learning

1 May 2019Jinglin ChenNan Jiang

Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for these methods often crucially rely on two types of assumptions: (1) mild distribution shift, and (2) representation conditions that are stronger than realizability... (read more)

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