Beyond Average Return in Markov Decision Processes

• URL: http://arxiv.org/abs/2310.20266v2
• Date: Mon, 19 Feb 2024 10:46:18 GMT
• Title: Beyond Average Return in Markov Decision Processes
• Authors: Alexandre Marthe (ENS de Lyon, UMPA-ENSL), Aur\'elien Garivier (UMPA-ENSL (MC2)), Claire Vernade
• Abstract summary: We prove that only generalized means can be optimized exactly, even in the more general framework of Distributional Reinforcement Learning (DistRL). We provide error bounds on the resulting estimators, and discuss the potential of this approach as well as its limitations.
• Score: 49.157108194438635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: What are the functionals of the reward that can be computed and optimized exactly in Markov Decision Processes?In the finite-horizon, undiscounted setting, Dynamic Programming (DP) can only handle these operations efficiently for certain classes of statistics. We summarize the characterization of these classes for policy evaluation, and give a new answer for the planning problem. Interestingly, we prove that only generalized means can be optimized exactly, even in the more general framework of Distributional Reinforcement Learning (DistRL).DistRL permits, however, to evaluate other functionals approximately. We provide error bounds on the resulting estimators, and discuss the potential of this approach as well as its limitations.These results contribute to advancing the theory of Markov Decision Processes by examining overall characteristics of the return, and particularly risk-conscious strategies.

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