Multiagent Learning through Neuroevolution (2012)
Neuroevolution is a promising approach for constructing intelligent agents in many complex tasks such as games, robotics, and decision making. It is also well suited for evolving team behavior for many multiagent tasks. However, new challenges and opportunities emerge in such tasks, including facilitating cooperation through reward sharing and communication, accelerating evolution through social learning, and measuring how good the resulting solutions are. This paper reviews recent progress in these three areas, and suggests avenues for future work.
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In J. Liu et al., editors, Advances in Computational Intelligence, LNCS 7311, 24-46, Berlin, Heidelberg:, 2012. Springer.
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Eliana Feasley Former Ph.D. Student elie [at] cs utexas edu
Leif Johnson leif [at] cs utexas edu
Igor V. Karpov Masters Alumni ikarpov [at] gmail com
Risto Miikkulainen Faculty risto [at] cs utexas edu
Padmini Rajagopalan Postdoctoral Alumni padminir [at] utexas edu
Aditya Rawal Ph.D. Alumni aditya [at] cs utexas edu
Wesley Tansey Former Collaborator tansey [at] cs utexas edu
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