Learning coordination strategies for multiple robots

The central issue in the design of multirobot systems is the coordination of the robots´ behaviors. Traditionally, this has been done by hand-coding complex strategies. Recent work has focussed on how strategies can be learned, but many of these systems suffer from convergence, credit assignment and scalability problems. This paper proposes a new approach for learning multirobot coordination strategies that addresses these concerns. The effectiveness of the technique is demonstrated using the noncooperative prisoners´ dilemma and the cooperative predator and prey domains