Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning Based Power control for Wireless Multicast
  Systems   We consider a multicast scheme recently proposed for a wireless downlink in
[1]. It was shown earlier that power control can significantly improve its
performance. However for this system, obtaining optimal power control is
intractable because of a very large state space. Therefore in this paper we use
deep reinforcement learning where we use function approximation of the
Q-function via a deep neural network. We show that optimal power control can be
learnt for reasonably large systems via this approach. The average power
constraint is ensured via a Lagrange multiplier, which is also learnt. Finally,
we demonstrate that a slight modification of the learning algorithm allows the
optimal control to track the time varying system statistics.

15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th