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.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/33476 |
| Date | 26 November 2012 |
| Creators | Ng, Larry |
| Contributors | Emami, Mohammad Reza |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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