Model-Assisted Deep Reinforcement Learning for Dynamic Wireless Scheduling

Recently Deep Reinforcement Learning (DRL) based techniques have been applied to scheduling algorithms at wireless Base Stations (BS) with promising results. The Deep Neural Networks (DNN) used in the DRL approaches have a fixed size input, and this poses a problem in real usage scenarios where the system state that includes number of users can be variable. In this paper we address this important problem by proposing a model-assisted DRL approach, which combines a model based solution with DRL based scheduling and can address both the variable input size and the DNN complexity issues. We show that this approach can achieve performance comparable to a DRL algorithm where a larger DNN trained for the worst-case scenario of the maximum number of users in the system. From a practical perspective our approach of using a model-assisted DRL has the following advantages: 1) There is no strict upper bound on the maximum number of users that can be supported by the system as opposed to the case when we train for the worst case number of users, 2) Training is faster and more energy efficient, and 3) leads to low-complex DNNs achieve comparable performance.