Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Online Learning in Kernelized Markov Decision Processes   We develop algorithms with low regret for learning episodic Markov decision
processes based on kernel approximation techniques. The algorithms are based on
both the Upper Confidence Bound (UCB) as well as Posterior or Thompson Sampling
(PSRL) philosophies, and work in the general setting of continuous state and
action spaces when the true unknown transition dynamics are assumed to have
smoothness induced by an appropriate Reproducing Kernel Hilbert Space (RKHS).

2
The categories of this paper are: math
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg