Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL   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.

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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom