Poster
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
Max Simchowitz · Kevin Jamieson
East Exhibition Hall B + C #203
Keywords: [ Algorithms -> Active Learning; Algorithms -> Bandit Algorithms; Theory ] [ Learning Theory ] [ Reinforcement Learning and Planning ] [ Markov Decision Processes ]
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Abstract
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Abstract:
This paper establishes that optimistic algorithms attain gap-dependent and non-asymptotic logarithmic regret for episodic MDPs. In contrast to prior work, our bounds do not suffer a dependence on diameter-like quantities or ergodicity, and smoothly interpolate between the gap dependent logarithmic-regret, and the $\widetilde{\mathcal{O}}(\sqrt{HSAT})$-minimax rate. The key technique in our analysis is a novel ``clipped'' regret decomposition which applies to a broad family of recent optimistic algorithms for episodic MDPs.
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