Masayuki Ohta
Tokyo Institute of Technology, Japan
Ladda ner artikel
Ingår i: RobocCup-99 Team Descriptions. Simulation League
Linköping Electronic Conference Proceedings 4:8, s. 36-39
Linköping Electronic Articles in Computer and Information Science vol. 4 4:8, p. 36-39
Publicerad: 1999-12-15
ISBN:
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
This paper describes the design of Gemini a client program for SoccerServer. The goal of Gemini is learning cooperative behaviors without direct communication in multi-agent environment. With recent implementation; it can select the best strategy against opponent; statistically.
[1] M.Ohta and T.Ando \Cooperative Reward in Reinforcement Learning" Proc. of 3rd JSAI RoboMech Symposia pages 7-11 April 1998.
[2] Grefenstette, John.J. \Credit Assignment in Rule Discoverry Systems Based on Genetic Algorithms" Machine Learning, Vol.3 pages 225-245 1988.
[3] Kaelbling L. P., Littman M. L. and Moore A. W. \Reinforcement Learning: A Survey" Journal of Arti cial Intelligence Research 4, pages 237-285 1996.
[4] Kimura H., Yamamura M. and Kobayashi S. \Reinforcement Learning in Partially Observable Markov Decision Processes: A Stochastic Gradient Method" Journal of Japanese Society for Arti cial Intelligence, Vol.11, No.5 papges 761-768 1996
