Despite the advancement of research and development on multi-robot teams, a key challenge still remains as to how to develop effective mechanisms that enable the robots to autonomously generate, adapt, and enhance team behaviours while improving their individual performance simultaneously. After a literature review of various multi-agent learning approaches, the two most promising learning paradigms, i.e., cooperative learning and advice sharing are adopted for future development. Although individually these methodologies may not provide a solution, their proper integration will provide a platform that allows for the incorporation of multi-agent learning with social behaviours. These methods are examined in relation to the performance characteristics of single-robot learning to ascertain if they retain viable learning characteristics despite the integration of individual learning into team behaviour. Further, various modifications to the Q-Learning algorithm were tested, and the best performing modification was implemented into the proposed multi robot learning approach.

Despite the advancement of research and development on multi-robot teams, a key challenge still remains as to how to develop effective mechanisms that enable the robots to autonomously generate, adapt, and enhance team behaviours while improving their individual performance simultaneously. After a literature review of various multi-agent learning approaches, the two most promising learning paradigms, i.e., cooperative learning and advice sharing are adopted for future development. Although individually these methodologies may not provide a solution, their proper integration will provide a platform that allows for the incorporation of multi-agent learning with social behaviours. These methods are examined in relation to the performance characteristics of single-robot learning to ascertain if they retain viable learning characteristics despite the integration of individual learning into team behaviour. Further, various modifications to the Q-Learning algorithm were tested, and the best performing modification was implemented into the proposed multi robot learning approach.