Introduction Machine learning (ML) [24] is a means of automatically generating solutions that perform better than those that are hand-coded by human programmers. Such improvement is possible in problem domains where optimal solutions are di#cult to identify, i.e., when there are no models available that can accurately relate a system's dynamics to its performance. One such domain is the control of multi-robot systems. Mobile robots are notoriously di#cult to control in a robust, reliable, and repeatable fashion. The challenges stem from uncertainty inherent in physically embodied systems, including in sensors, e#ectors, and interactions between the system components and the environment. The behavior-based (BB) control paradigm [22, 1] provides a means of structuring robot controllers into collections of task-achieving modules or behaviors, such as exploration and obstacle avoidance. The modules operate in parallel and interact within the system and also through their e#ects on the en