Resource Allocation in Wireless Networks With Deep Reinforcement Learning: A Circumstance-Independent Approach

In the conventional approaches using reinforcement learning (RL) for resource allocation in wireless networks, the structure of the policy depends on network circumstances such as the number of users and quality-of-service requirements. Due to this dependence, the policy is hard to be used in a practical system where the network circumstance is dynamically changing. To resolve this issue, we propose a circumstance-independent policy that can effectively address the different network circumstances even with a single policy. Thus, contrary to the conventional RL approaches, the proposed policy can be easily applied in the practical system. We then develop a deep RL algorithm to learn it. Through simulation results, we show that a single proposed policy can be used over different circumstances, and it achieves a close performance to the circumstance-dependent policy for each circumstance, which learns the optimal policy for the corresponding circumstance.