Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a  task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task.
 The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.
We present MetaMimic, an algorithm that takes as input a demonstration dataset and outputs (i) a one-shot high-fidelity imitation policy (ii) an unconditional task policy. The paper looks at the problem of one-shot imitation with high accuracy of imitation, extending DDPGfD to use only state trajectories. This paper proposes an approach for one-shot imitation with high accuracy, and addresses the common problem of exploration in imitation learning. Presents an RL method for learning from video demonstration without access to expert actions