事件驱动的强化学习多智能体编队控制

针对经典强化学习的多智能体编队存在通信和计算资源消耗大的问题，本文引入事件驱动控制机制，智能体的动作决策无须按固定周期进行，而依赖于事件驱动条件更新智能体动作。在设计事件驱动条件时，不仅考虑智能体的累积奖赏值，还引入智能体与邻居奖赏值的偏差，智能体间通过交互来寻求最优联合策略实现编队。数值仿真结果表明，基于事件驱动的强化学习多智能体编队控制算法，在保证系统性能的情况下，能有效降低多智能体的动作决策频率和资源消耗。

Event-triggered reinforcement learning formation control for multi-agent

A large consumption of communication and computing capabilities has been reported in classical reinforcement learning of multi-agent formation. This paper introduces an event-triggered mechanism so that the multi-agent’s decisions do not need to be carried out periodically; instead, the multi-agent’s actions are replaced depending on the event-triggered condition. Both the sum of total reward and variance in current rewards are considered when designing an event-triggered condition, so a joint optimization strategy is obtained by exchanging information among multiple agents. Numerical simulation results demonstrate that the multi-agent formation control algorithm can effectively reduce the frequency of a multi-agent’s action decisions and consumption of resources while ensuring system performance.