Collaborative Decision-Making Technique for Wireless Communication Means Based on Multi-Agent Reinforcement Learning
Kun Zhang, Chao Hu, Deqing Huang, Jiachen Shen · 2024
Wireless communication is a pivotal domain within modern information and communication technology. With the proliferation of mobile devices and the Internet of Things, wireless communication methods have become ubiquitous across diverse industries. Challenges arise in collaborative decision-making technology that leverages wireless communication, including heightened information variability, increased time sensitivity, and a significant need for manual intervention. This paper introduces, for the first time, the application of multi -agent reinforcement learning technology to develop wireless communication agents. This approach involves distributed training, real-time simulation interaction, and other key technologies rooted in the pertinent wireless communication mechanism. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method.