  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.
