Model-Based Reinforcement Learning for Wireless Channel Access

In this paper, we study a wireless channel access method using model-based reinforcement learning in limited spectral resources. The proposed method maximizes sampling efficiency by learning an environment and using it for actor learning. The environment is a model that considers a dynamic packet queue in a situation where a wireless channel is shared. We show the performance results of the proposed learning algorithm in the considered environment.