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• W3202898482 abstract "Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to couple tightly with the model-learning procedure. Hence the learned models may lack the ability of being re-used with more specialized planners. In this paper we address this issue and provide approaches to learn an RL model efficiently without the guidance of a reward signal. In particular, we take a plug-in solver approach, where we focus on learning a model in the exploration phase and demand that emph{any planning algorithm} on the learned model can give a near-optimal policy. Specicially, we focus on the linear mixture MDP setting, where the probability transition matrix is a (unknown) convex combination of a set of existing models. We show that, by establishing a novel exploration algorithm, the plug-in approach learns a model by taking $tilde{O}(d^2H^3/epsilon^2)$ interactions with the environment and emph{any} $epsilon$-optimal planner on the model gives an $O(epsilon)$-optimal policy on the original model. This sample complexity matches lower bounds for non-plug-in approaches and is emph{statistically optimal}. We achieve this result by leveraging a careful maximum total-variance bound using Bernstein inequality and properties specified to linear mixture MDP." @default.
• W3202898482 created "2021-10-11" @default.
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• W3202898482 date "2021-10-07" @default.
• W3202898482 modified "2023-09-23" @default.
• W3202898482 title "Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver" @default.
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• W3202898482 doi "https://doi.org/10.48550/arxiv.2110.03244" @default.
• W3202898482 hasPublicationYear "2021" @default.
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