考虑工人路径的多智能体强化学习空间众包任务分配方法

针对工人和任务进行匹配是空间众包研究的核心问题之一,但已有的方法通常会忽略工人路径对任务分配结果产生的影响.传统的任务分配方法存在计算速度慢、适用范围小和协作效果不突出等问题.对此,从空间众包平台的角度出发研究面向路网的空间众包任务分配问题,以任务完成时间最短为目标,提出考虑工人路径规划的基于多智能体强化学习的QMIX-A*算法,缩短任务的平均完成时间,进而提高用户的满意度.大量的数值仿真研究验证了QMIX-A*的有效性和稳定性,为空间众包服务平台的任务分配与路径优化策略的选择提供决策支持.

A multi-agent reinforcement learning algorithm for spatial crowdsourcing task assignments considering workers' path

Matching tasks and workers is one of the core problems in spatial crowdsourcing research, but the impact of path planning of workers on task allocation results is usually ignored in the existing literature. There are problems with traditional task assignment methods including slow computing speed, small application scope, and unremarkable collaboration effect. From the perspective of a spatial crowdsourcing platform, this research is oriented toward the spatial crowdsourcing task assignment problem on the road networks and puts forward a QMIX-A* algorithm based on multi-agent reinforcement learning considering workers'' path planning. The proposed approach with the minimum completion time of tasks as the objective can shorten the tasks'' average completion time, thereby improving users'' satisfaction. The effectiveness and stability of the QMIX-A* are verified by a large number of simulation studies. The results of the research can provide decision support for the task allocation and path optimization strategy selection of spatial crowdsourcing service platforms.