Strategy Recovery for Stochastic Mean Payoff Games

TL;DR

This paper demonstrates that recovering optimal strategies in stochastic mean payoff games is computationally as difficult as solving the games themselves, answering a longstanding open question.

Contribution

It proves the complexity of strategy recovery in stochastic mean payoff games, establishing that it is as hard as solving the games, resolving a question posed by researchers.

Findings

Strategy recovery is as hard as solving the game

Addresses an open problem in game theory

Clarifies the computational complexity of strategy extraction

Abstract

We prove that to find optimal positional strategies for stochastic mean payoff games when the value of every state of the game is known, in general, is as hard as solving such games tout court. This answers a question posed by Daniel Andersson and Peter Bro Miltersen.