Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning with Symmetric Prior for Predictive Power
  Allocation to Mobile Users   Deep reinforcement learning has been applied for a variety of wireless tasks,
which is however known with high training and inference complexity. In this
paper, we resort to deep deterministic policy gradient (DDPG) algorithm to
optimize predictive power allocation among K mobile users requesting video
streaming, which minimizes the energy consumption of the network under the
no-stalling constraint of each user. To reduce the sampling complexity and
model size of the DDPG, we exploit a kind of symmetric prior inherent in the
actor and critic networks: permutation invariant and equivariant properties, to
design the neural networks. Our analysis shows that the free model parameters
of the DDPG can be compressed by 2/K^2. Simulation results demonstrate that the
episodes required by the learning model with the symmetric prior to achieve the
same performance as the vanilla policy reduces by about one third when K = 10.

24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an