Abstract
For path planning of Automated Guided Vehicle (AGV), an improved neural network based on Q-learning algorithm is proposed to overcome the problems of slow convergence speed, too many iterations and unstable convergence performance existing in the traditional Q-learning algorithm. The idea is to introduce a convolutional neural network (CNN) into Q-learning algorithm to achieve the value function approximation of Q-learning algorithm and solve the dimension disaster problem in practical application. The improved CNN based Q-learning algorithm is more convenient for the selection of action and the update of Q. The simulation results show that the improved CNN based Q-learning algorithm is stable and has faster convergence speed and less iteration times.
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