Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Expert-Guided Symmetry Detection in Markov Decision Processes   Learning a Markov Decision Process (MDP) from a fixed batch of trajectories
is a non-trivial task whose outcome's quality depends on both the amount and
the diversity of the sampled regions of the state-action space. Yet, many MDPs
are endowed with invariant reward and transition functions with respect to some
transformations of the current state and action. Being able to detect and
exploit these structures could benefit not only the learning of the MDP but
also the computation of its subsequent optimal control policy. In this work we
propose a paradigm, based on Density Estimation methods, that aims to detect
the presence of some already supposed transformations of the state-action space
for which the MDP dynamics is invariant. We tested the proposed approach in a
discrete toroidal grid environment and in two notorious environments of
OpenAI's Gym Learning Suite. The results demonstrate that the model
distributional shift is reduced when the dataset is augmented with the data
obtained by using the detected symmetries, allowing for a more thorough and
data-efficient learning of the transition functions.

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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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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: gr-qc
4
The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin