Poster
Multi-Agent Generative Adversarial Imitation Learning
Jiaming Song · Hongyu Ren · Dorsa Sadigh · Stefano Ermon
Room 517 AB #157
Keywords: [ Adversarial Networks ] [ Generative Models ] [ Multi-Agent RL ] [ Reinforcement Learning ]
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.
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