Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments

Runlong Zhou, Zihan Zhang, Simon S. Du

ICML 2023

We provide a systematic study of variance-dependent regret bounds of model-based and model-free reinforcement learning for tabular MDPs. The proposed model-based algorithm is both optimal for stochastic and deterministic MDPs.

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