Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recursive Sparse Pseudo-input Gaussian Process SARSA   The class of Gaussian Process (GP) methods for Temporal Difference learning
has shown promise for data-efficient model-free Reinforcement Learning. In this
paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse
Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas
for its predictive moments. This extension promotes greater memory efficiency,
since previous computations can be reused and, interestingly, it provides a
technique for updating value estimates on a multiple timescales

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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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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: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
3
The categories of this paper are: physics