Technical Note: \cal Q-Learning
Machine Learning
Introduction: The Challenge of Reinforcement Learning
Machine Learning
Intelligent Traffic Signal Control Approach Based on Fuzzy-Genetic Algorithm
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
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In order to reduce the delay of vehicles passing through junction, the signal timing of agent controlled intersection was optimized by Q-Learning approach. On the basis of fuzzy rule set, the effect of signal control was improved through optimizing the combination of control rules with Q-Learning. The result of simulation illustrates that the signal control method based on Q-Learning is better than fixed-time control, actuated control and signal control based on genetic algorithms. The result of this research indicates that the signal control method based on Q-Learning is adapted to the urban traffic control.