Mobile robot local path planning based on Q reinforcement learning and CMAC

被引：0

作者：

Wang Zhongmin ^{[1
]}

Yue Hong ^{[1
]}

机构：

[1] Hebei Univ Technol, Inst Robot & Automat, Tianjin 300130, Peoples R China

来源：

Proceedings of the 24th Chinese Control Conference, Vols 1 and 2 | 2005年

关键词：

mobile robot; local path planning; Q reinforcement learning; credit assignment; CMAC;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper Q reinforcement learning algorithm is adopted for mobile robot local path planning. It makes mobile robot resolve the problem of local path planning in a complex environment. By using CMAC neural network based on credit assignment this algorithm is implemented, and conventional CMAC's online learning speed and its accuracy are improved at the same time. Simulation experiments prove that this algorithm introduced adapt any complex environment and own good self- learning abilities.

引用

页码：1494 / 1496

页数：3