  In this paper, we aim to maximize the SSR for heterogeneous service demands
in the cooperative MEC-assisted RAN slicing system by jointly considering the
multi-node computing resources cooperation and allocation, the transmission
resource blocks (RBs) allocation, and the time-varying dynamicity of the
system. To this end, we abstract the system into a weighted undirected topology
graph and, then propose a recurrent graph reinforcement learning (RGRL)
algorithm to intelligently learn the optimal hybrid RA policy. Therein, the
graph neural network (GCN) and the deep deterministic policy gradient (DDPG) is
combined to effectively extract spatial features from the equivalent topology
graph. Furthermore, a novel time recurrent reinforcement learning framework is
designed in the proposed RGRL algorithm by incorporating the action output of
the policy network at the previous moment into the state input of the policy
network at the subsequent moment, so as to cope with the time-varying and
contextual network environment. In addition, we explore two use case scenarios
to discuss the universal superiority of the proposed RGRL algorithm. Simulation
results demonstrate the superiority of the proposed algorithm in terms of the
average SSR, the performance stability, and the network complexity.
