Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Aggressive Q-Learning with Ensembles: Achieving Both High Sample
  Efficiency and High Asymptotic Performance   Recently, Truncated Quantile Critics (TQC), using distributional
representation of critics, was shown to provide state-of-the-art asymptotic
training performance on all environments from the MuJoCo continuous control
benchmark suite. Also recently, Randomized Ensemble Double Q-Learning (REDQ),
using a high update-to-data ratio and target randomization, was shown to
achieve high sample efficiency that is competitive with state-of-the-art
model-based methods. In this paper, we propose a novel model-free algorithm,
Aggressive Q-Learning with Ensembles (AQE), which improves the
sample-efficiency performance of REDQ and the asymptotic performance of TQC,
thereby providing overall state-of-the-art performance during all stages of
training. Moreover, AQE is very simple, requiring neither distributional
representation of critics nor target randomization.

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The categories of this paper are: nlin
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The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin