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.

这篇研究论文介绍了一种名为 Hidden Agenda 的社交推理游戏，用于研究多代理合作中的合作决策和学习智能体在未知背景下的团队合作。通过强化学习算法，这些代理可以学习各种行为，包括在没有语言交流的情况下的搭档和投票。

Hidden Agenda：具有不同学到均衡的社交推断游戏