The invention relates to a heterogeneous cloud 
wireless access network resource allocation method based on deep 
reinforcement learning, and belongs to the technical field of mobile communication. Themethod comprises the following steps: 1) taking 
queue stability as a constraint, combining congestion control, user association, 
subcarrier allocation and power allocation, and establishing a 
random optimization model for maximizing the total 
throughput of the network; 2) considering the complexity of the scheduling problem, the 
state space and the action space of the 
system are high-dimensional,and the DRL 
algorithm uses a neural network as a 
nonlinear approximation function to efficiently solve the problem of dimensionality disasters; and 3) aiming at the complexity and the dynamic variability of the 
wireless network environment, introducing a transfer learning 
algorithm, and utilizing the 
small sample learning characteristics of transfer learning to enable the DRL 
algorithm to obtain an optimal 
resource allocation strategy under the condition of a small number of samples. According to the method, the total 
throughput of the whole network can be maximized, and meanwhile, the requirement of service 
queue stability is met. And the method has a very high application value in a mobile communication 
system.