Imitation Learning by Reinforcement Learning

2021-08-10 · ICLR 2022 4 · Kamil Ciosek

Imitation learning algorithms learn a policy from demonstrations of expert behavior. We show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning with a stationary reward. Our theoretical analysis both certifies the recovery of expert reward and bounds the total variation distance between the expert and the imitation learner, showing a link to adversarial imitation learning. We conduct experiments which confirm that our reduction works well in practice for continuous control tasks.

📄 PDF Abstract BibTeX arXiv:2108.04763

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spotify-research/il-by-rl 공식 구현 pytorch

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continuous-control Continuous ControlImitation Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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