Research on Multi-Agent Collaborative Scheduling Planning Method for Time-Triggered Networks
Changsheng Chen, Anrong Zhao, Zhihao Zhang, Tao Zhang, Chao Fan · Electronics · 2025
Time-triggered Ethernet combines time-triggered and event-triggered communication, and is suitable for fields with high real-time requirements. Aiming at the problem that the traditional scheduling algorithm is not effective in scheduling event-triggered messages, a message scheduling algorithm based on multi-agent reinforcement learning (MADDPG, Multi-Agent Deep Deterministic Policy Gradient) and a hybrid algorithm combining SMT (Satisfiability Modulo Theories) solver and MADDPG are proposed. This method aims to optimize the scheduling of event-triggered messages while maintaining the uniformity of time-triggered message scheduling, providing more time slots for event-triggered messages, and reducing their waiting time and end-to-end delay. Through the designed scheduling software, in the experiment, compared with the SMT-based algorithm and the traditional DQN (Deep Q-Network) algorithm, the new method shows better load balance and lower message jitter, and it is verified in the OPNET simulation environment that it can effectively reduce the delay of event-triggered messages.