Learning heterogeneous behaviors for robots to cooperate in the performance of a task is a difficult problem. Evolving the separate team members in a single chromosome limits the capacity of the genetic algorithm to learn. Evolving the separate team members in separate populations promotes specialization and gives the genetic algorithm more flexibility to produce a solution, but can be either computationally prohibitive or result in credit assignment complications. In this paper, we apply punctuated anytime learning to assist in the co-evolution of separate team member populations. A box-pushing task is used to show the success of this method.
CITATION STYLE
Parker, G. B., & Blumenthal, H. J. (2002). Punctuated anytime learning for evolving a team. In Robotics, Automation, Control and Manufacturing: Trends, Principles and Applications - Proceedings of the 5th Biannual World Automation Congress, WAC 2002, ISORA 2002, ISIAC 2002 and ISOMA 2002 (Vol. 14, pp. 559–566). https://doi.org/10.1109/wac.2002.1049496
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