Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning for Distributed Uncoordinated Cognitive
  Radios Resource Allocation   This paper presents a novel deep reinforcement learning-based resource
allocation technique for the multi-agent environment presented by a cognitive
radio network that coexists through underlay dynamic spectrum access (DSA) with
a primary network. The resource allocation technique presented in this work is
distributed, not requiring coordination with other agents. The presented
algorithm is the first deep reinforcement learning technique for which
convergence to equilibrium policies can be shown in the non-stationary
multi-agent environment that results from the uncoordinated dynamic interaction
between radios through the shared wireless environment. Moreover, simulation
results show that in a finite learning time the presented technique is able to
find policies that yield performance within 3 % of an exhaustive search
solution, finding the optimal policy in nearly 70 % of cases. Moreover, it is
shown that standard single-agent deep reinforcement learning may not achieve
convergence when used in a non-coordinated, coupled multi-radio scenario.

34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph