Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Opponent Aware Reinforcement Learning   We introduce Threatened Markov Decision Processes (TMDPs) as an extension of
the classical Markov Decision Process framework for Reinforcement Learning
(RL). TMDPs allow suporting a decision maker against potential opponents in a
RL context. We also propose a level-k thinking scheme resulting in a novel
learning approach to deal with TMDPs. After introducing our framework and
deriving theoretical results, relevant empirical evidence is given via
extensive experiments, showing the benefits of accounting for adversaries in RL
while the agent learns

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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc