Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning

16 Dec 2019Andrea CelliMarco CicconeRaffaele BongoNicola Gatti

Many real-world applications involve teams of agents that have to coordinate their actions to reach a common goal against potential adversaries. This paper focuses on zero-sum games where a team of players faces an opponent, as is the case, for example, in Bridge, collusion in poker, and collusion in bidding... (read more)

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