Solving decentralized multi-agent control problems with genetic algorithms

In decentralized control of multi-agent systems each agent is making a decision regarding its action autonomously, based on its own observations. In the light of the formal models of decentralized environments presented in the last decade, finding an optimal solution to a decentralized control problem is computationally prohibitive, even for moderately complicated environments. The problem, however, is of great significance since many of the real world systems can be treated as multi-agent systems with decentralized control. In this article, the authors propose an approximate algorithm for the problem based on a genetic algorithm. First, the problem is formalized using decentralized partially observable Markov decision processes. Then a way of representing a solution (joint policy) in a chromosome is introduced and a genetic algorithm is proposed as a search mechanism. Finally, a multi-agent tiger problem is used as an experimental framework to show the effectiveness of the algorithm.