西安电子科技大学学报（自然科学版）2019，Vol.46Issue(1)：46-50,63,6.DOI:10.19665/j.issn1001-2400.2019.01.008
一种改进dueling网络的机器人避障方法
Method for robot obstacle avoidance based on the improved dueling network
摘要
Abstract
In view of the disadvantages of traditional reinforcement learning methods in motion planning, especially the problem of robot obstacle avoidance,it is easy to have overestimation and difficult to adapt to complex environment.A new model based on deep reinforcement learning is proposed to improve the obstacle avoidance performance of robots.The model combines dueling networks with Q-learning which is the traditional reinforcement learning method,and using two independent trained dueling networks to deal with environmental data and predict the action value.In the output layer,the state value and the action advantage are output respectively,with both values combined as the final action value.The model can process high dimension data to adapt to complex and changeable environment,and output advantageous actions for robot selection to get a higher accumulative reward.It can effectively improve the obstacle avoidance performance of a robot.关键词
机器人避障/深度增强学习/dueling网络/独立训练Key words
robot obstacle avoidance/deep reinforcement learning/dueling networks/independent trained分类
信息技术与安全科学引用本文复制引用
周翼,陈渤..一种改进dueling网络的机器人避障方法[J].西安电子科技大学学报（自然科学版）,2019,46(1):46-50,63,6.基金项目
国家自然科学基金(61771361) （61771361）
国家自然科学基金杰出青年基金(61525105) （61525105）
