Multi-Agent Cooperation-Based Deep Reinforcement Learning for Multisensor Perception Communication System in HSR Tunnel Scenario

The rapid development of High-Speed Railway (HSR) puts higher requirements on comprehensive perception and reliable transmission in tunnel scenarios. To realize efficient and reliable perception information transmission of HSR in the tunnel, we propose a multisensor perception communication system, which consists of an Access Point (AP) deployed on each carriage for perception information transmission and self-powered wireless sensors. The AP remote transmits the perception information through the leaky cable deployed in the tunnel. We construct an optimization problem for minimizing the transmission time of the whole system’s perception information in the multi-network system and the adjacent area of the carriage. A Multi-Agent Cooperation-based Deep Reinforcement Learning (MA-CDRL) algorithm is proposed to get the optimal scheduling strategy for reducing the transmission time. We construct the CDRL neural network for the algorithm to introduce the states of other APs, resulting in the system making more efficient transmission strategies. In the simulations, the proposed algorithm gets a better performance than the comparison algorithms and is verified in various dynamic HSR scenarios, such as different travel speeds and sensor distributions.