Online Policy Evaluation for MDPs with Dynamic UBSR Measures

• URL: http://arxiv.org/abs/2607.23030v1
• Date: Sat, 25 Jul 2026 04:12:05 GMT
• Title: Online Policy Evaluation for MDPs with Dynamic UBSR Measures
• Abstract summary: We propose efficient online learning algorithms for policy evaluation in Markov decision processes.<n>We introduce the UBSR-TD algorithm, establish conditions under which it converges almost surely, and develop several variants designed to accelerate convergence.<n>Our formulation shows that existing policy evaluation algorithms for risk-neutral MDPs can be readily adapted to dynamic UBSR settings.
• Score: 5.271072635937926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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