Generative Adversarial Imitation Learning - NeurIPS

Generative Adversarial Imitation LearningJonathan ErmonStanford Learning a policy from example expert behavior, without interaction withthe expert or access to a reinforcement signal. One approach is to recover theexpert s cost function with inverse reinforcement Learning , then extract a policyfrom that cost function with reinforcement Learning . This approach is indirectand can be slow. We propose a new general framework for directly extracting apolicy from data as if it were obtained by reinforcement Learning following inversereinforcement Learning . We show that a certain instantiation of our frameworkdraws an analogy between Imitation Learning and Generative Adversarial networks,from which we derive a model-free Imitation Learning algorithm that obtains signif-icant performance gains over existing model-free methods in imitating complexbehaviors in large, high-dimensional IntroductionWe are interested in a specific setting of Imitation Learning the problem of Learning to perform atask from expert demonstrations in which the learner is given only samples of trajectories fromthe expert, is not allowed to query the expert for more data while training , and is not provided areinforcement signal of any kind.

modeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where we find that it outperforms competing methods by a wide margin in training policies for complex,

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  Training, Learning, Adversarial, Generative, Imitation, Generative adversarial imitation learning, Adversarial training

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