Online Policy Evaluation for MDPs with Dynamic UBSR Measures

Abstract:Developing efficient function-approximation methods for policy evaluation is a fundamental challenge in risk-aware reinforcement learning. Existing approaches either focus on restrictive classes of risk measures or rely on access to a simulator, limiting their applicability in fully online settings. In this work, we propose computationally efficient online learning algorithms for policy evaluation in Markov decision processes (MDPs) with dynamic utility-based shortfall risk (UBSR) measures under linear function approximation. Specifically, we introduce the UBSR-TD algorithm, establish conditions under which it converges almost surely, and develop several variants designed to accelerate convergence. Our formulation shows that existing policy evaluation algorithms for risk-neutral MDPs can be readily adapted to dynamic UBSR settings by incorporating a loss function into the temporal-difference error. Numerical experiments support our theoretical findings, and an application to a perishable inventory management problem with shelf-life uncertainty demonstrates the practical effectiveness of the proposed methods.

Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2607.23030 [cs.LG]

(or arXiv:2607.23030v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.23030 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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