Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Group Equivariant Deep Reinforcement Learning   In Reinforcement Learning (RL), Convolutional Neural Networks(CNNs) have been
successfully applied as function approximators in Deep Q-Learning algorithms,
which seek to learn action-value functions and policies in various
environments. However, to date, there has been little work on the learning of
symmetry-transformation equivariant representations of the input environment
state. In this paper, we propose the use of Equivariant CNNs to train RL agents
and study their inductive bias for transformation equivariant Q-value
approximation. We demonstrate that equivariant architectures can dramatically
enhance the performance and sample efficiency of RL agents in a highly
symmetric environment while requiring fewer parameters. Additionally, we show
that they are robust to changes in the environment caused by affine
transformations.

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The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat