Application of Reinforcement Learning Based on Neural Network to Dynamic Obstacle Avoidance

摘要：

This paper focuses on the application of reinforcement learning to obstacle avoidance in dynamic environments.Behavior-based control architecture is more robust and better in real-time performance than conventional model based architecture in the control of mobile robot.An intelligent controller is proposed by integrating reinforcement learning with the behavior-based control architecture and applied to the obstacle avoidance.Neural network is used to approximate the Q-function to store the Q-value.By using the reinforcement learning,the mobile robot can learn to select proper behavior online without knowing the exact model of the system.In experiments,dynamic and static obstacles are placed in the environments separately.Experiment results show that the mobile robot can get to the target point without colliding with any obstacle after a period of learning.

作者: Junfei Qiao Zhanjun Hou Xiaogang Ruan

作者单位: College of Electronic Information and Control Engineering Beijing University of Technology Pingleyuan No.100,Chaoyang District,Beijing,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 784-788

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）