Time-triggered communication scheduling method based on reinforcement learning

In the future, time-triggered communication mechanism will be more widely selected for information transmission to ensure the certainty of information interaction in avionics system. How to reasonably implement time-triggered communication scheduling design is the key to time-triggered application to avionics interconnection systems. For the periodic task of time-triggered scheduling, we proposed a method for generating periodic scheduling timetable based on reinforcement learning. Firstly, the traffic scheduling task is transformed into a tree search problem, which has the Markov characteristics needed for reinforcement learning. Then, the reinforcement learning algorithm based on neural network is used to explore the schedule, and the waiting time is shortened to optimize the schedule. As the training is completed, the model can be directly used in tasks with similar message distribution. Compared with the method, e.g. Yices, which uses the satisfiability modulo theories (SMT) to solve the time-triggered schedule, the proposed method does not cause undetermined problem, and can guarantee the correctness and optimization of the time-triggered scheduling design results. For a large network with 1 000 messages, the calculation speed of the proposed method is dozens of times faster than that of the SMT, and meanwhile, the end-to-end delay of the generated message by scheduling is less than 1% of that of the SMT, which greatly improves the timeliness of message transmission.