Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex
  Envelopes   Stochastic Approximation (SA) is a popular approach for solving fixed-point
equations where the information is corrupted by noise. In this paper, we
consider an SA involving a contraction mapping with respect to an arbitrary
norm, and show its finite-sample error bounds while using different stepsizes.
The idea is to construct a smooth Lyapunov function using the generalized
Moreau envelope, and show that the iterates of SA have negative drift with
respect to that Lyapunov function. Our result is applicable in Reinforcement
Learning (RL). In particular, we use it to establish the first-known
convergence rate of the V-trace algorithm for off-policy TD-learning. Moreover,
we also use it to study TD-learning in the on-policy setting, and recover the
existing state-of-the-art results for $Q$-learning. Importantly, our
construction results in only a logarithmic dependence of the convergence bound
on the size of the state-space.

14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn