Solving Common-Payoff Games with Approximate Policy Iteration

TL;DR

This paper introduces CAPI, a new algorithm that combines common knowledge with deep reinforcement learning to discover optimal joint policies in small common-payoff games, outperforming existing methods in accuracy.

Contribution

CAPI is a novel algorithm that prioritizes discovering optimal joint policies over scalability, improving upon prior methods like BAD in small game settings.

Findings

CAPI successfully finds optimal joint policies in small common-payoff games.

CAPI outperforms other multi-agent reinforcement learning algorithms in accuracy.

CAPI does not scale to large games like Hanabi, but excels in smaller settings.

Abstract

For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, decentralized control is difficult -- computing even an epsilon-optimal joint policy is a NEXP complete problem. Nevertheless, a recently rediscovered insight -- that a team of agents can coordinate via common knowledge -- has given rise to algorithms capable of finding optimal joint policies in small common-payoff games. The Bayesian action decoder (BAD) leverages this insight and deep reinforcement learning to scale to games as large as two-player Hanabi. However, the approximations it uses to do so prevent it from discovering optimal joint policies even in games small enough to brute force optimal solutions. This work proposes CAPI, a novel algorithm which, like BAD, combines common knowledge with deep reinforcement learning. However, unlike BAD, CAPI prioritizes the propensity to discover optimal joint policies over scalability. While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so. Code is available at https://github.com/ssokota/capi .