Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Worst-Case Regret Bounds for Randomized Least-Squares Value
  Iteration   This paper studies regret minimization with randomized value functions in
reinforcement learning. In tabular finite-horizon Markov Decision Processes, we
introduce a clipping variant of one classical Thompson Sampling (TS)-like
algorithm, randomized least-squares value iteration (RLSVI). Our
$\tilde{\mathrm{O}}(H^2S\sqrt{AT})$ high-probability worst-case regret bound
improves the previous sharpest worst-case regret bounds for RLSVI and matches
the existing state-of-the-art worst-case TS-based regret bounds.

25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
9
The categories of this paper are: nlin