Stationary Policies are Optimal in Risk-averse Total-reward MDPs with EVaR

• URL: http://arxiv.org/abs/2408.17286v1
• Date: Fri, 30 Aug 2024 13:33:18 GMT
• Title: Stationary Policies are Optimal in Risk-averse Total-reward MDPs with EVaR
• Authors: Xihong Su, Marek Petrik, Julien Grand-Clément,
• Abstract summary: We show that the risk-averse em total reward criterion can be optimized by a stationary policy. Our results indicate that the total reward criterion may be preferable to the discounted criterion in a broad range of risk-averse reinforcement learning domains.
• Score: 12.719528972742394
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Optimizing risk-averse objectives in discounted MDPs is challenging because most models do not admit direct dynamic programming equations and require complex history-dependent policies. In this paper, we show that the risk-averse {\em total reward criterion}, under the Entropic Risk Measure (ERM) and Entropic Value at Risk (EVaR) risk measures, can be optimized by a stationary policy, making it simple to analyze, interpret, and deploy. We propose exponential value iteration, policy iteration, and linear programming to compute optimal policies. In comparison with prior work, our results only require the relatively mild condition of transient MDPs and allow for {\em both} positive and negative rewards. Our results indicate that the total reward criterion may be preferable to the discounted criterion in a broad range of risk-averse reinforcement learning domains.

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