Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uniform-PAC Bounds for Reinforcement Learning with Linear Function
  Approximation   We study reinforcement learning (RL) with linear function approximation.
Existing algorithms for this problem only have high-probability regret and/or
Probably Approximately Correct (PAC) sample complexity guarantees, which cannot
guarantee the convergence to the optimal policy. In this paper, in order to
overcome the limitation of existing algorithms, we propose a new algorithm
called FLUTE, which enjoys uniform-PAC convergence to the optimal policy with
high probability. The uniform-PAC guarantee is the strongest possible guarantee
for reinforcement learning in the literature, which can directly imply both PAC
and high probability regret bounds, making our algorithm superior to all
existing algorithms with linear function approximation. At the core of our
algorithm is a novel minimax value function estimator and a multi-level
partition scheme to select the training samples from historical observations.
Both of these techniques are new and of independent interest.

25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org