Imitation Bootstrapped Reinforcement Learning

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Imitation Bootstrapped Reinforcement Learning Hengyuan Hu Stanford University Suvir Mirchandani Stanford Univeristy Dorsa Sadigh Stanford University Abstract Despite the considerable potential of reinforcement learning (RL), robotic control tasks predominantly rely on imitation learning (IL) due to its better sample efficiency. However, it is costly to collect comprehensive expert demonstrations that enable IL to generalize to all possible scenarios, and any distribution shift would require recollecting data for finetuning. Therefore, RL is appealing if it can build upon IL as an efficient aut

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