  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.
