Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Symmetry Learning for Function Approximation in Reinforcement Learning   In this paper we explore methods to exploit symmetries for ensuring sample
efficiency in reinforcement learning (RL), this problem deserves ever
increasing attention with the recent advances in the use of deep networks for
complex RL tasks which require large amount of training data. We introduce a
novel method to detect symmetries using reward trails observed during episodic
experience and prove its completeness. We also provide a framework to
incorporate the discovered symmetries for functional approximation. Finally we
show that the use of potential based reward shaping is especially effective for
our symmetry exploitation mechanism. Experiments on various classical problems
show that our method improves the learning performance significantly by
utilizing symmetry information.

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The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio