Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems   Restless bandit problems are instances of non-stationary multi-armed bandits.
These problems have been studied well from the optimization perspective, where
the goal is to efficiently find a near-optimal policy when system parameters
are known. However, very few papers adopt a learning perspective, where the
parameters are unknown. In this paper, we analyze the performance of Thompson
sampling in episodic restless bandits with unknown parameters. We consider a
general policy map to define our competitor and prove an
$\tilde{\mathcal{O}}(\sqrt{T})$ Bayesian regret bound. Our competitor is
flexible enough to represent various benchmarks including the best fixed action
policy, the optimal policy, the Whittle index policy, or the myopic policy. We
also present empirical results that support our theoretical findings.

4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th