  Deep reinforcement learning (DRL) for resource allocation has been
investigated extensively owing to its ability of handling model-free and
end-to-end problems. Yet the high training complexity of DRL hinders its
practical use in dynamic wireless systems. To reduce the training cost, we
resort to graph reinforcement learning for exploiting two kinds of relational
priors inherent in many problems in wireless communications: topology
information and permutation properties. To design graph reinforcement learning
framework systematically for harnessing the two priors, we first conceive a
method to transform state matrix into state graph, and then propose a general
method for graph neural networks to satisfy desirable permutation properties.
To demonstrate how to apply the proposed methods, we take deep deterministic
policy gradient (DDPG) as an example for optimizing two representative resource
allocation problems. One is predictive power allocation that minimizes the
energy consumed for ensuring the quality-ofservice of each user that requests
video streaming. The other is link scheduling that maximizes the sum-rate for
device-to-device communications. Simulation results show that the graph DDPG
algorithm converges much faster and needs much lower space complexity than
existing DDPG algorithms to achieve the same learning performance.
