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We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies that map directly from RGB camera inputs to joint velocities. We demonstrate that our approach can solve a wide variety of visuomotor tasks, for which engineering a scripted controller would be laborious. In experiments, our reinforcement and imitationdoi:10.15607/rss.2018.xiv.009 dblp:conf/rss/Zhu0MRECTKHFH18 fatcat:u6pt5wi6lvgchhezcsn6qntid4