Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study

Author:

Kalyanakrishnan Shivaram,Liu Yaxin,Stone Peter

Publisher

Springer Berlin Heidelberg

Reference13 articles.

1. Bradtke, S.J., Duff, M.O.: Reinforcement learning methods for continuous-time Markov decision problems. In: Advances in Neural Information Processing Systems 7 (NIPS-94) (1995)

2. Chen, M., Foroughi, E., Heintz, F., Huang, Z., Kapetanakis, S., Kostiadis, K., Kummeneje, J., Noda, I., Obst, O., Riley, P., Steffens, T., Wang, Y., Yin, X.: Users Manual: RoboCup Soccer Server — for Soccer Server Version 7.07 and Later. The RoboCup Federation (August 2002)

3. Geipel, M.M.: Informed and advice-taking reinforcement learning for simulated robot soccer. Master’s thesis, Fakultät für Informatik, Forschungs- und Lehreinheit Informatik IX, Technische Universität München (2005)

4. Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E., Matsubara, H.: RoboCup: A challenge problem for AI. AI Magazine 18(1), 73–85 (1997)

5. Kok, J.R., Vlassis, N., Groen, F.C.A.: UvA Trilearn 2003 team description. In: Proceedings CD RoboCup 2003 (2003)

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