  A key challenge in the study of multiagent cooperation is the need for
individual agents not only to cooperate effectively, but to decide with whom to
cooperate. This is particularly critical in situations when other agents have
hidden, possibly misaligned motivations and goals. Social deduction games offer
an avenue to study how individuals might learn to synthesize potentially
unreliable information about others, and elucidate their true motivations. In
this work, we present Hidden Agenda, a two-team social deduction game that
provides a 2D environment for studying learning agents in scenarios of unknown
team alignment. The environment admits a rich set of strategies for both teams.
Reinforcement learning agents trained in Hidden Agenda show that agents can
learn a variety of behaviors, including partnering and voting without need for
communication in natural language.
