Multi-Agent Generative Adversarial Imitation Learning

Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon

Abstract: We propose a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse reinforcement learning. We introduce a practical multi-agent actor-critic algorithm with good empirical performance. Our method can be used to imitate complex behaviors in high-dimensional environments with multiple cooperative or competitive agents.

TL;DR: We perform Inverse RL in general-sum Markov games with a new Multi-agent Actor Critic algorithm.