Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast rates for online learning in Linearly Solvable Markov Decision
  Processes   We study the problem of online learning in a class of Markov decision
processes known as linearly solvable MDPs. In the stationary version of this
problem, a learner interacts with its environment by directly controlling the
state transitions, attempting to balance a fixed state-dependent cost and a
certain smooth cost penalizing extreme control inputs. In the current paper, we
consider an online setting where the state costs may change arbitrarily between
consecutive rounds, and the learner only observes the costs at the end of each
respective round. We are interested in constructing algorithms for the learner
that guarantee small regret against the best stationary control policy chosen
in full knowledge of the cost sequence. Our main result is showing that the
smoothness of the control cost enables the simple algorithm of following the
leader to achieve a regret of order $\log^2 T$ after $T$ rounds, vastly
improving on the best known regret bound of order $T^{3/4}$ for this setting.

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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
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The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th