Implications of Centralized and Distributed Multi-Agent Deep Reinforcement Learning in Dynamic Spectrum Access

Abstract

Multi-agent Deep Reinforcement Learning (MADRL) has been applied to a plethora of state-of-the-art applications such as resource allocations and network routing in both centralized and distributed manners. This paper investigates the performance of centralized and distributed MADRL in Dynamic Spectrum Access (DSA). We consider a multichannel wireless network with a shared bandwidth divided into k channels. The objective of the MADRL is to develop a spectrum access strategy by learning in both a centralized and distributed manner. To evaluate the performance of centralized and distributed MADRL, we tackle the spectrum access problem by applying centralized MADRL and distributed MADRL. Experimental results show that distributed MADRL outperforms the centralized MADRL by 15% in collision avoidance and accumulated rewards in DSA.