Neural network-based reinforcement learning applied to obstacle avoidance

Abstract

An intelligent control architecture with reinforcement learning was designed based on a behavior-based architecture to improve the learning ability of mobile robots.Normal tabular Q-learning can only be applied to discrete states and requires a large memory.Since neural networks have good generalization,a Q-learning system was developed based on a neural network for obstacle avoidance of mobile robots.Experiments show that the mobile robot can then learn to avoid obstacles.