基于事件驱动的多智能体强化学习研究

发布时间：2018-04-27 23:25

本文选题：事件驱动 + 多智能体　；参考：《智能系统学报》2017年01期

【摘要】：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。
[Abstract]:Aiming at the problems of communication and computing resource consumption in multi-agent reinforcement learning, this paper proposes an event-driven multi-agent reinforcement learning algorithm, which focuses on the event-driven learning strategy layer of multi-agent learning. In the process of interaction between agent and environment, the algorithm is based on the idea of event driven, and the trigger function is designed according to the change rate of the information observed by the agent, so that the communication and learning timing in the learning process do not need to be carried out in real time or on a periodic basis. Therefore, in the same time can reduce the number of data transmission and calculation. In addition, the computational resource consumption of the algorithm is analyzed, and the convergence of the algorithm is demonstrated. Finally, the simulation results show that the algorithm can reduce the number of times of communication and the number of policy traversal in the learning process, and then reduce the consumption of communication and computing resources.
【作者单位】：西南交通大学电气工程学院;
【基金】：国家自然科学基金青年项目(61304166)
【分类号】：TP181

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