Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. 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 further 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 competing agents.
@article{arxiv.1807.09936,
title = {Multi-Agent Generative Adversarial Imitation Learning},
author = {Jiaming Song and Hongyu Ren and Dorsa Sadigh and Stefano Ermon},
journal= {arXiv preprint arXiv:1807.09936},
year = {2018}
}