Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Empirical Evaluation of Contextual Policy Search with a Comparison-based
  Surrogate Model and Active Covariance Matrix Adaptation   Contextual policy search (CPS) is a class of multi-task reinforcement
learning algorithms that is particularly useful for robotic applications. A
recent state-of-the-art method is Contextual Covariance Matrix Adaptation
Evolution Strategies (C-CMA-ES). It is based on the standard black-box
optimization algorithm CMA-ES. There are two useful extensions of CMA-ES that
we will transfer to C-CMA-ES and evaluate empirically: ACM-ES, which uses a
comparison-based surrogate model, and aCMA-ES, which uses an active update of
the covariance matrix. We will show that improvements with these methods can be
impressive in terms of sample-efficiency, although this is not relevant any
more for the robotic domain.

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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio