Multi-Agent Generative Adversarial Imitation LearningDownload PDF

12 Feb 2018 (modified: 05 May 2023)ICLR 2018 Workshop SubmissionReaders: Everyone
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.
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