Learning policy representations in multiagent systems

Researchers proposed an unsupervised method for learning agent policy representations in multiagent systems.

The approach uses an objective inspired by imitation learning and agent identification to model behavior from limited interaction data.

It was tested in competitive continuous-control and cooperative communication tasks, where it helped with prediction, clustering, and policy optimization.

The framework offers a general way to infer and represent agent behavior, improving analysis and decision-making in multiagent settings.