Technical Note: \cal Q-Learning
Machine Learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent's Internal Model
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multiagent reinforcement learning using function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Recently, multiagent systems and data mining have attracted considerable attention in the computer science community. This paper combines these two hot research areas to introduce the term multiagent association rule mining on a cooperative learning system, which investigates employing data mining on a cooperative multiagent system. Learning in a partially observable and dynamic multiagent systems environment still constitutes a difficult and major research problem that is worth further investigation. Reinforcement learning has been proposed as a strong method for learning in multi-agent systems. So far, many researchers have proposed various methods to improve the learning ability in multiagent systems. However, reinforcement learning still has some drawbacks. One drawback is not modeling other learning agents present in the domain as part of the state of the environment. Another drawback is that even in learning case, some state-action pairs are experienced much less than others. In order to handle these problems, we describe a new action selection model based on association rules mining. Experimental results obtained on a well-known pursuit domain show the applicability, robustness and effectiveness of the proposed learning approach.