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Recent advances in deep reinforcement learning (RL) have led to considerableprogress in many 2-player zero-sum games, such as Go, Poker and Starcraft. Thepurely adversarial nature of such games allows for conceptually simple andprincipled application of RL methods. However real-world settings aremany-agent, and agent interactions are complex mixtures of common-interest andcompetitive aspects. We consider Diplomacy, a 7-player board game designed toaccentuate dilemmas resulting from many-agent interactions. It also features alarge combinatorial action space and simultaneous moves, which are challengingfor RL algorithms. We propose a simple yet effective approximate best responseoperator, designed to handle large combinatorial action spaces and simultaneousmoves. We also introduce a family of policy iteration methods that approximatefictitious play. With these methods, we successfully apply RL to Diplomacy: weshow that our agents convincingly outperform the previous state-of-the-art, andgame theoretic equilibrium analysis shows that the new process yieldsconsistent improvements.

Learning to Play No-Press Diplomacy with Best Response Policy Iteration