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Multi-Agent Generative Adversarial Imitation Learning
Jiaming Song · Hongyu Ren · Dorsa Sadigh · Stefano Ermon

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #157

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.

Author Information

Jiaming Song (Stanford University)

I am a first year Ph.D. student in Stanford University. I think about problems in machine learning and deep learning under the supervision of Stefano Ermon. I did my undergrad at Tsinghua University, where I was lucky enough to collaborate with Jun Zhu and Lawrence Carin on scalable Bayesian machine learning.

Hongyu Ren (Stanford University)
Dorsa Sadigh (Stanford)
Stefano Ermon (Stanford)

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