Earticle

초록

영어

For path planning of mobile robot, the traditional Q learning algorithm easy to fall into local optimum, slow convergence etc. issues, this paper proposes a new greedy strategy, multi-target searching of Q learning algorithm. Don't need to create the environment model, the mobile robot from a single-target searching transform into multi-target searching an unknown environment, firstly, by the dynamic greedy strategy exploring interim to use unknown environment, improve learning ability that mobile robot learn the environment, improve the convergence of the mobile robot speed. And a large number of improved Q-learning algorithms are applied to mobile robot optimization simulation in unknown environment, by comparing with traditional Q algorithm, theory and experiment proved that improved Q-learning algorithm speed up the convergence rate of the robot, improve collision avoidance capability and learning efficiency.

목차

Abstract
 1. Introduction
 2. Q Reinforcement Learning
 3. Dynamic Greedy Strategy
 4. Multi-target Search
 5. Improved Q Learning Algorithm is as Follows
 6. Simulation
 7. Main Text
 Acknowledgements
 References

키워드

저자

• Jiansheng Peng [ Guangxi Colleges and Universities Key Laboratory Breeding Base of System Control and Information Processing Hechi University, Yizhou 546300, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605