Anytime-Constrained Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2410.23637v1
• Date: Thu, 31 Oct 2024 05:07:01 GMT
• Title: Anytime-Constrained Multi-Agent Reinforcement Learning
• Authors: Jeremy McMahan, Xiaojin Zhu,
• Abstract summary: We introduce anytime constraints to the multi-agent setting with the corresponding solution being anytime-constrained equilibrium (ACE) We present a comprehensive theory of anytime-constrained Markov games, which includes a computational characterization of feasible policies. We also first theory of efficient computation for action-constrained Markov games, which may be of independent interest.
• Score: 6.981971551979697
• License:
• Abstract: We introduce anytime constraints to the multi-agent setting with the corresponding solution concept being anytime-constrained equilibrium (ACE). Then, we present a comprehensive theory of anytime-constrained Markov games, which includes (1) a computational characterization of feasible policies, (2) a fixed-parameter tractable algorithm for computing ACE, and (3) a polynomial-time algorithm for approximately computing feasible ACE. Since computing a feasible policy is NP-hard even for two-player zero-sum games, our approximation guarantees are the best possible under worst-case analysis. We also develop the first theory of efficient computation for action-constrained Markov games, which may be of independent interest.

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