Multi-Agent Linear-Quadratic-Gaussian Systems with “Poor” Observations

Abstract

Very few solutions are available for multi-agent control problems which have a nonclassical information structure. This is due to the fact that the agents can use their control actions not only for control itself, but also to pass information to other agents. We consider decentralized control situations in which information transfer is comparitively difficult because of large noise on some of the observation variables. By obtaining appropriate limits to this information transfer, linear control laws derived by disregarding the “poor” observations are shown to be “close” to optimal. It is then possible to derive other linear laws, from a similar situation again with static information structure, which are an order of magnitude closer, both in respect of the laws themselves and the optimized costs. It is shown however that iteration of this process in a natural way does not produce substantially better results.