Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mastering Atari Games with Limited Data   Reinforcement learning has achieved great success in many applications.
However, sample efficiency remains a key challenge, with prominent methods
requiring millions (or even billions) of environment steps to train. Recently,
there has been significant progress in sample efficient image-based RL
algorithms; however, consistent human-level performance on the Atari game
benchmark remains an elusive goal. We propose a sample efficient model-based
visual RL algorithm built on MuZero, which we name EfficientZero. Our method
achieves 194.3% mean human performance and 109.0% median performance on the
Atari 100k benchmark with only two hours of real-time game experience and
outperforms the state SAC in some tasks on the DMControl 100k benchmark. This
is the first time an algorithm achieves super-human performance on Atari games
with such little data. EfficientZero's performance is also close to DQN's
performance at 200 million frames while we consume 500 times less data.
EfficientZero's low sample complexity and high performance can bring RL closer
to real-world applicability. We implement our algorithm in an
easy-to-understand manner and it is available at
https://github.com/YeWR/EfficientZero. We hope it will accelerate the research
of MCTS-based RL algorithms in the wider community.

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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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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: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-bio