Generative Adversarial Imitation Learning.pdf
cs.stanford.edu · 6,275 words · saved by 1 readers
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Generative Adversarial Imitation Learning Jonathan Ho Stefano Ermon OpenAI Stanford University hoj@openai.com ermon@cs.stanford.edu Abstract 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…
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