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A key challenge in the study of multiagent cooperation is the need forindividual agents not only to cooperate effectively, but to decide with whom tocooperate. This is particularly critical in situations when other agents havehidden, possibly misaligned motivations and goals. Social deduction games offeran avenue to study how individuals might learn to synthesize potentiallyunreliable information about others, and elucidate their true motivations. Inthis work, we present Hidden Agenda, a two-team social deduction game thatprovides a 2D environment for studying learning agents in scenarios of unknownteam alignment. The environment admits a rich set of strategies for both teams.Reinforcement learning agents trained in Hidden Agenda show that agents canlearn a variety of behaviors, including partnering and voting without need forcommunication in natural language.

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria