Learning complementary multiagent behaviors: A case study

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5949 LNAI, Issue , 2010, Pages 153-165

  Kalyanakrishnan, Shivaram ^a   Stone, Peter ^a  

^a University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX TASK; EMPIRICAL RESULTS; KEEPAWAY; LEARNING CAPABILITIES; MACHINE-LEARNING; MULTI-AGENT; MULTI-AGENT REINFORCEMENT LEARNING; MULTIPLE AGENTS; POLICY SEARCH; ROBOT SOCCER; SIMULATION SOCCER; SOCCER TEAM; TEMPORAL DIFFERENCE LEARNING; TIGHTLY-COUPLED; TURN-TAKING;

MULTI AGENT SYSTEMS; REINFORCEMENT;

LEARNING ALGORITHMS;

EID: 77950988223     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-11876-0_14     Document Type: Conference Paper

References (14)

1. Benda, M., Jagannathan, V., Dodhiawala, R.: On optimal cooperation of knowledge sources - an empirical investigation. Technical Report BCS-G2010-28, Boeing Advanced Technology Center, Boeing Comp. Serv., Seattle, WA (July 1986)
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. De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.: A tutorial on the cross-entropy method. Annals of Operations Research 134(1), 19-67 (2005)
4. Ghavamzadeh, M., Mahadevan, S., Makar, R.: Hierarchical multi-agent reinforcement learning. Aut. Agents and Multi-Agent Sys. 13(2), 197-229 (2006)
5. Guestrin, C., Lagoudakis, M.G., Parr, R.: Coordinated reinforcement learning. In: Sammut, C., Hoffmann, A.G. (eds.) Proceedings of the Nineteenth International Conference on Machine Learning, University of New South Wales, Sydney, Australia, July 8-12, pp. 227-234. Morgan Kaufmann, San Francisco (2002)
6. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1998)
7. Haynes, T., Wainwright, R., Sen, S., Schoenefeld, D.: Strongly typed genetic programming in evolving cooperation strategies. In: Forrest, S. (ed.) Proc. of the 6th Int. Conf. Gen. Alg., San Mateo,CA, pp. 271-278. Morgan Kaufman, San Francisco (1995)
8. Iscen, A., Erogul, U.: A new perpective to the keepaway soccer: The takers. In: Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 1341-1344. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2008)
9. Jung, T., Polani, D.: Learning Robocup-Keepaway with kernels. In: Gaussian Processes in Practice: JMLR Workshop and Conference Proceedings, vol.1, pp. 33-57 (2007)
10. Metzen, J.H., Edgington, M., Kassahun, Y., Kirchner, F.: Analysis of an evolutionary reinforcement learning method in a multiagent domain. In: Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 291-298. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2008)
11. Riedmiller, M., Gabel, T.: On experiences in a complex and competitive gaming domain: Reinforcement learning meets robocup. In: 3rd IEEE Symposium on Computational Intelligence and Games, April 2007, pp. 17-23 (2007)
12. Rosin, C.D., Belew, R.K.:Methods for competitive co-evolution: Finding opponents worth beating. In: Forrest, S. (ed.) Proc. of the 6th Int. Conf. Gen. Alg., pp. 373-380. Morgan Kaufmann, San Mateo (1995)
13. Stone, P.: Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer. MIT Press, Cambridge (2000)
14. Stone, P., Sutton, R.S., Kuhlmann, G.: Reinforcement learning for RoboCup-soccer keepaway. Adaptive Behavior 13(3), 165-188 (2005)