Technical Note: \cal Q-Learning
Machine Learning
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Layered learning in multiagent systems
Layered learning in multiagent systems
Reinforcement Learning of Player Agents in RoboCup Soccer Simulation
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
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In most cases, agent learning tends to be a good method for solving challenging problems in multi-agent System (MAS). Since the learning efficiency is significantly different according to the actions taken by each specific agent, suitable algorithms will play important roles in the answer of the mentioned problems in multi-agent system. Although many related work are addressed to different algorithms of agent learning, few of them could balance efficiency and accuracy. In this paper, a hybrid Q-learning algorithm named CE-NNR which is springed form the CEQ learning and NNR Q-learning is presented. The algorithm is then well extended to RoboCup soccer simulation system and is proved to be reasonable with the experimental results arranged at the end of this paper.