By Topic

Reinforcement learning for mobile robot: From reaction to deliberation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chunlin, Chen ; Dept. of Automation, Univ. of Science and Technology of China, Hefei 230027, P. R. China ; Zonghai, Chen

Reinforcement learning has been widely used for mobile robot learning and control. Some progress of this kind of approaches is surveyed and argued in a new way which emphasizes on different levels of algorithms according to different complexity of tasks. The central conjecture is that approaches which combine reactive and deliberative control to robotics scale better to complex real-world applications than purely reactive or deliberative ones. This paper describes basic reactive reinforcement learning algorithms and two classes of approaches to achieve deliberation, which are modular methods and hierarchical methods. By combining reactive and deliberative paradigms, the whole system gains advantages from different control levels. The paper gives results of experiments as a case study to verify the effectiveness of the proposed approaches.

Published in:

Systems Engineering and Electronics, Journal of  (Volume:16 ,  Issue: 3 )