Title: Learning Policy Representations in Multiagent Systems,
Abstract: Modeling agent behavior is central to understanding the emergence of complex
phenomena in multiagent systems. Prior work in agent modeling has largely been
task-specific and driven by hand-engineering domain-specific prior knowledge.
We propose a general learning framework for modeling agent behavior in any
multiagent system using only a handful of interaction data. Our framework casts
agent modeling as a representation learning problem. Consequently, we construct
a novel objective inspired by imitation learning and agent identification and
design an algorithm for unsupervised learning of representations of agent
policies. We demonstrate empirically the utility of the proposed framework in
(i) a challenging high-dimensional competitive environment for continuous
control and (ii) a cooperative environment for communication, on supervised
predictive tasks, unsupervised clustering, and policy optimization using deep
reinforcement learning.