Visualizing Multi-Agent Reinforcement Learning for Robotic Communication in Industrial IoT Networks

With its mobility and flexibility, autonomous robots have received extensive attention in industrial Internet of Things (IoT). In this paper, we adopt non-orthogonal multiple access and multi-antenna technology to enhance the connectivity of sensors and the throughput of data collection through taking advantage of the power and spatial domains. For average sumrate maximization, we optimize the transmit power of sensors and the trajectories of robots jointly. To deal with uncertainty and dynamics in the industrial environment, we propose a multiagent reinforcement learning (MARL) algorithm with experience exchange. Next, we present the visualization of robotic communication and mobility to analyze the learning behavior intuitively. From the software implementation results, we observe that the proposed MARL algorithm can effectively adjust the communication strategies of sensors and control the trajectories of robots in a fully distributed manner. The code and demonstrations can be found at https://github.com/lry-bupt/Visual_MARL.