Abstract
Large state spaces and incomplete information are two problems that stand out in learning in multi-agent systems. In this paper we tackle them both by using a combination of decision trees and Bayesian networks (BNs) to model the environment and the Q-function. Simulated robotic soccer is used as a testbed, since there agents are faced with both large state spaces and incomplete information. The long-term goal of this research is to define generic techniques that allow agents to learn in large-scaled multi-agent systems.
Chapter PDF
Similar content being viewed by others
Keywords
- Bayesian Network
- Reinforcement Learn
- Incomplete Information
- Multiagent System
- Joint Probability Distribution
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Proc. UAI (1996)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Hu, J., Wellman, M.P.: Multiagent reinforcement learning in stochastic games (1999) (submitted for publication)
Kostiadis, K., Hu, H.: Reinforcement Learning and Co-operation in a Simulated Multi-agent System. In: Proc. of IEEE/RJS IROS 1999, Korea (1999)
Litmann, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proceedings of the Eleventh International Conference on Machine Learning, pp. 157–163 (1994)
Moore, A.W., Atkeson, C.: The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State Space. Machine Learning Journal 21 (1995)
Noda, I., Matsubara, H., Hiraki, K., Frank, I.: Soccer Server: A Tool for Research om Multiagent Systems. Applied Artificial Intelligence 12, 233–250 (1998)
Nowe, A., Verbeeck, K.: Learning Automata and Pareto Optimality for Coordination in MAS. Technical report, COMO, Vrije Universiteit Brussel (2000)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Pyeatt, L.D., Howe, A.E.: Decision Tree Function Approximation in Reinforcement Learning. Technical Report CS-98-112, Colorado State University (1998)
Russell, S., Norvig, P.: Artificial Intelligence: a Modern Approach. Series in Artificial Intelligence. Prentice Hall, Englewood Cliffs, New Jersey (1995)
Stone, P.: Layered Learning in Multiagent Systems. A Winning Approach to Robotic Soccer. MIT Press, Cambridge (2000)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tuyls, K., Maes, S., Manderick, B. (2003). Reinforcement Learning in Large State Spaces. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds) RoboCup 2002: Robot Soccer World Cup VI. RoboCup 2002. Lecture Notes in Computer Science(), vol 2752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45135-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-45135-8_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40666-2
Online ISBN: 978-3-540-45135-8
eBook Packages: Springer Book Archive