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Many real-world scenarios involve teams of agents that have to coordinatetheir actions to reach a shared goal. We focus on the setting in which a teamof agents faces an opponent in a zero-sum, imperfect-information game. Teammembers can coordinate their strategies before the beginning of the game, butare unable to communicate during the playing phase of the game. This is thecase, for example, in Bridge, collusion in poker, and collusion in bidding. Inthis setting, model-free RL methods are oftentimes unable to capturecoordination because agents' policies are executed in a decentralized fashion.Our first contribution is a game-theoretic centralized training regimen toeffectively perform trajectory sampling so as to foster team coordination. Whenteam members can observe each other actions, we show that this approachprovably yields equilibrium strategies. Then, we introduce a signaling-basedframework to represent team coordinated strategies given a buffer of pastexperiences. Each team member's policy is parametrized as a neural networkwhose output is conditioned on a suitable exogenous signal, drawn from alearned probability distribution. By combining these two elements, weempirically show convergence to coordinated equilibria in cases where previousstate-of-the-art multi-agent RL algorithms did not.

Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies