A distributed stochastic optimal control solution is presented for cooperative multi-agent systems. The network of agents is partitioned into multiple factorial subsystems, each of which consists of a central agent and neighboring agents. Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems. When solving for the local control actions, the joint optimality equation for each subsystem is cast as a linear partial differential equation and solved using the Feynman-Kac formula. The solution and the optimal control action are then formulated as path integrals and approximated by a Monte-Carlo method. Numerical verification is provided through a simulation example consisting of a team of cooperative UAVs.

翻译：对合作型多试剂系统提出了分布式随机最佳控制解决方案,将各种物剂网络分成多个元素子系统,每个物剂由一个中央剂和邻近物剂组成,地方控制行动仅依靠物剂的当地观测,目的是优化子系统的共同成本功能。当解决地方控制行动时,每个子系统的联合最佳方程式是线性局部方程式,用Feynman-Kac公式解决,然后将溶剂和最佳控制行动作为路径构件,以Monte-Carlo方法相近,通过一个由合作型无人驾驶飞行器组成的模拟示例进行数字核查。