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Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
Kavya Kopparapu · Edgar Dueñez-Guzman · Jayd Matyas · Alexander Vezhnevets · John Agapiou · Kevin McKee · Richard Everett · Janusz Marecki · Joel Leibo · Thore Graepel

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in Cooperative AI »
Event URL: https://openreview.net/forum?id=bR4YBbDIsd »

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.

Author Information

Kavya Kopparapu (Harvard University)
Edgar Dueñez-Guzman (DeepMind)
Jayd Matyas
Alexander Vezhnevets
John Agapiou (Google DeepMind)
Kevin McKee (DeepMind)
Richard Everett (DeepMind)
Janusz Marecki
Joel Leibo (DeepMind)
Thore Graepel (DeepMind)

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