An algorithm for cooperative probabilistic control design

This paper deals with the decentralized closed loop control in a pure probabilistic framework. In this framework, a system is a controlled Markov chain whose transition probabilities depend on the actions of the agents. The agents are also described in a probabilistic way. The objective is to drive the system so that the joint state and agents actions are close to a set of given target probability distributions. The Kullback-Leibler divergence is used as a performance measure. The resulting algorithm uses dynamic programming interleaved with an iterative process that computes the behavior of each agent.