Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zap Q-Learning for Optimal Stopping Time Problems   The objective in this paper is to obtain fast converging reinforcement
learning algorithms to approximate solutions to the problem of discounted cost
optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on
a compact subset of $\mathbb{R}^n$. We build on the dynamic programming
approach taken by Tsitsikilis and Van Roy, wherein they propose a Q-learning
algorithm to estimate the optimal state-action value function, which then
defines an optimal stopping rule. We provide insights as to why the convergence
rate of this algorithm can be slow, and propose a fast-converging alternative,
the "Zap-Q-learning" algorithm, designed to achieve optimal rate of
convergence. For the first time, we prove the convergence of the Zap-Q-learning
algorithm under the assumption of linear function approximation setting. We use
ODE analysis for the proof, and the optimal asymptotic variance property of the
algorithm is reflected via fast convergence in a finance example.

27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int