UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms

• URL: http://arxiv.org/abs/2105.02135v4
• Date: Mon, 07 Oct 2024 15:27:58 GMT
• Title: UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms
• Authors: Ilya Levin, Denis Belomestny, Alexey Naumov, Sergey Samsonov,
• Abstract summary: Policy evaluation is an important instrument for the comparison of different algorithms in Reinforcement Learning (RL) We present a novel model-free upper value iteration procedure $(sf UVIP)$ that allows us to estimate the suboptimality gap $Vstar(x) - Vpi(x)$ from above and to construct confidence intervals for $Vstar$. We provide theoretical guarantees for $sf UVIP$ under general assumptions and illustrate its performance on a number of benchmark RL problems.
• Score: 5.582861438320171
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• Abstract: Policy evaluation is an important instrument for the comparison of different algorithms in Reinforcement Learning (RL). Yet even a precise knowledge of the value function $V^{\pi}$ corresponding to a policy $\pi$ does not provide reliable information on how far is the policy $\pi$ from the optimal one. We present a novel model-free upper value iteration procedure $({\sf UVIP})$ that allows us to estimate the suboptimality gap $V^{\star}(x) - V^{\pi}(x)$ from above and to construct confidence intervals for $V^\star$. Our approach relies on upper bounds to the solution of the Bellman optimality equation via martingale approach. We provide theoretical guarantees for ${\sf UVIP}$ under general assumptions and illustrate its performance on a number of benchmark RL problems.

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