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Poster
Generative Adversarial Imitation Learning
Jonathan Ho · Stefano Ermon

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #40 #None
in Tuesday Posters »

Consider learning a policy from example expert behavior, without interaction with the expert or access to a reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.

Keywords: Deep Learning or Neural Networks · Reinforcement Learning Algorithms · (Other) Robotics and Control

Author Information

Jonathan Ho (Stanford)
Stefano Ermon (Stanford)

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