基于事件驱动的多智能体强化学习研究

本文针对多智能体强化学习中存在的通信和计算资源消耗大等问题，提出了一种基于事件驱动的多智能体强化学习算法，侧重于事件驱动在多智能体学习策略层方面的研究。在智能体与环境的交互过程中，算法基于事件驱动的思想，根据智能体观测信息的变化率设计触发函数，使学习过程中的通信和学习时机无需实时或按周期地进行，故在相同时间内可以降低数据传输和计算次数。另外，分析了该算法的计算资源消耗，以及对算法收敛性进行了论证。最后，仿真实验说明了该算法可以在学习过程中减少一定的通信次数和策略遍历次数，进而缓解了通信和计算资源消耗。

Reinforcement learning for event-triggered multi-agent systems

Focusing on the existing multi-agent reinforcement learning problems such as huge consumption of communication and calculation, a novel event-triggered multi-agent reinforcement learning algorithm was presented. The algorithm focused on an event-triggered idea at the strategic level of multi-agent learning. In particular, during the interactive process between agents and the learning environment, the communication and learning were triggered through the change rate of observation.Using an appropriate event-triggered design, the discontinuous threshold was employed, and thus real-time or periodical communication and learning can be avoided, and the number of communications and calculations were reduced within the same time. Moreover, the consumption of computing resource and the convergence of the proposed algorithm were analyzed and proven. Finally, the simulation results show that the number of communications and traversals were reduced in learning, thus saving the computing and communication resources.