Anytime-Constrained Equilibria in Polynomial Time
Machine Learning
2025-03-05 v2 Artificial Intelligence
Data Structures and Algorithms
Computer Science and Game Theory
Abstract
We extend anytime constraints to the Markov game setting and the corresponding solution concept of an anytime-constrained equilibrium (ACE). Then, we present a comprehensive theory of anytime-constrained equilibria that 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 ACE. Since computing a feasible policy is NP-hard even for two-player zero-sum games, our approximation guarantees are optimal so long as . We also develop the first theory of efficient computation for action-constrained Markov games, which may be of independent interest.
Keywords
Cite
@article{arxiv.2410.23637,
title = {Anytime-Constrained Equilibria in Polynomial Time},
author = {Jeremy McMahan},
journal= {arXiv preprint arXiv:2410.23637},
year = {2025}
}