Reinforcement Learning via Implicit Imitation Guidance

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Reinforcement Learning via Implicit Imitation Guidance Perry Dong*, Alec M. Lessing*, Annie S. Chen*, Chelsea Finn Stanford University; {perryd, aleclessing, asc8}@stanford.edu Abstract We study the problem of sample efficient reinforcement learning, where prior data such as demonstrations are provided for initialization in lieu of a dense reward signal. A natural approach is to incorporate an imitation learning objective, either as regularization during training or to acquire a reference policy. However, imitation learning objectives can ultimately degrade long-term performance, as it does no

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