Uniform Value and Decidability in Ergodic Blind Stochastic Games

TL;DR

This paper introduces ergodic blind stochastic games, proving the existence of a uniform value and its approximability, which is a novel result even for POMDPs, and shows the value's independence from initial beliefs.

Contribution

The paper establishes the existence and approximability of the uniform value in ergodic blind stochastic games, a significant advancement in understanding these games and POMDPs.

Findings

Proves the existence of the uniform value in ergodic blind stochastic games.

Provides an algorithm to approximate the uniform value, demonstrating decidability.

Shows the uniform value is independent of initial beliefs.

Abstract

We study a class of two-player zero-sum stochastic games known as \textit{blind stochastic games}, where players neither observe the state nor receive any information about it during the game. A central concept for analyzing long-duration stochastic games is the \textit{uniform value}. A game has a uniform value $v$ if for every $\varepsilon>0$, Player 1 (resp., Player 2) has a strategy such that, for all sufficiently large $n$, his average payoff over $n$ stages is at least $v-\varepsilon$ (resp., at most $v+\varepsilon$). Prior work has shown that the uniform value may not exist in general blind stochastic games. To address this, we introduce a subclass called \textit{ergodic blind stochastic games}, defined by imposing an ergodicity condition on the state transitions. For this subclass, we prove the existence of the uniform value and provide an algorithm to approximate it, establishing the \textit{decidability} of the approximation problem. Notably, this decidability result is novel even in the single-player setting of Partially Observable Markov Decision Processes (POMDPs). Furthermore, we show that no algorithm can compute the uniform value exactly, emphasizing the tightness of our result. Finally, we establish that the uniform value is independent of the initial belief.