Generative Adversarial Imitation Learning

NeurIPS 2016 Jonathan HoStefano Ermon

Consider learning a policy from example expert behavior, without interaction with the expert or access to 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... (read more)

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