Ant Colony Optimization Incorporated With Fuzzy Q-Learning for Reinforcement Fuzzy Control

Chia-Feng Juang, Chun-Ming Lu
2009 IEEE transactions on systems, man and cybernetics. Part A. Systems and humans  
Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper
more » ... ack Backer-Upper problem -a reinforcement fuzzy control problem-shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy-Q Learning.
doi:10.1109/tsmca.2009.2014539 fatcat:k3mgypvwvne53mk5nyhjegnysi