针对典型的端到端通信策略不能决定通信间隔时间，只能在固定频率下通信的问题，提出一种基于深度强化学习方法的事件触发变频率通信策略，以解决多无人车协同最小通信问题。首先建立事件触发架构，主要包含计算通信的控制器，并给出触发条件，保证满足条件时多无人车间进行通信，大幅度减少通信总量。其次，基于多智能体深度确定性策略梯度(multiple agent deep deterministic policy gradient，MADDPG)算法对触发机制进行优化，提高算法收敛速度。仿真和实车实验表明，随着迭代次数的增加，在完成协同任务的前提下，多无人车系统中通信数据量降低了 55。74％，验证了所提出策略的有效性。
Event-triggered communication of multiple unmanned ground vehicles collaborative based on MADDPG
In response to the problem of typical end-to-end communication strategies that cannot determine the communication interval and can only communicate at fixed frequencies,an event-triggered communication strategy is proposed based on deep reinforcement learning to solve the minimal communication problem in multi-unmanned ground vehicles collaboration.Firstly,an event-triggered architecture is established,which mainly includes a communication controller and provides trigger conditions.This ensures that communication occurs among multiple unmanned ground vehicle only when the conditions are met,significantly reducing the overall commu-nication volume.Secondly,the trigger mechanism is optimized using the multiple agent deep deterministic policy gradient(MADDPG)algorithm,which improves the convergence speed of the algorithm.Simulation and real vehicle experiments show that with increasing iterations,the amount of communication data in the multiple unmanned ground vehicle system is reduced by 55.74％while still accomplishing the collaborative tasks,thus validating the effecti-veness of the proposed strategy.
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