Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On convergence rates of game theoretic reinforcement learning algorithms   This paper investigates a class of multi-player discrete games where each
player aims to maximize its own utility function. Each player does not know the
other players' action sets, their deployed actions or the structures of its own
or the others' utility functions. Instead, each player only knows its own
deployed actions and its received utility values in recent history. We propose
a reinforcement learning algorithm which converges to the set of action
profiles which have maximal stochastic potential with probability one.
Furthermore, the convergence rate of the proposed algorithm is quantified. The
algorithm performance is verified using two case studies in the smart grid and
cybersecurity.

14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph