Solving Mean-Payoff Games via Quasi Dominions

TL;DR

This paper introduces a new algorithm for solving mean-payoff games by combining small progress measures and quasi dominions, resulting in significantly faster practical performance without increasing worst-case complexity.

Contribution

It presents a novel algorithm that merges concepts from parity game solutions to improve efficiency in solving mean-payoff games.

Findings

Algorithm outperforms existing solutions by orders of magnitude in experiments.

No increase in worst-case complexity compared to previous algorithms.

Integration of concepts accelerates convergence to solutions.

Abstract

We propose a novel algorithm for the solution of mean-payoff games that merges together two seemingly unrelated concepts introduced in the context of parity games, small progress measures and quasi dominions. We show that the integration of the two notions can be highly beneficial and significantly speeds up convergence to the problem solution. Experiments show that the resulting algorithm performs orders of magnitude better than the asymptotically-best solution algorithm currently known, without sacrificing on the worst-case complexity.