By Topic

Motion simulation of robot arm using reinforcement learning

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 $13
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)
Oshiro, T. ; Hosei Univ., Tokyo ; Watanabe, K.

This paper describes the learning of robot arm action by reinforcement learning. We used Q-learning, which is a typical method of reinforcement learning, and which was programmed via MATLAB software. Simulations demonstrated the shortest path of robot arm motion to reach the target location.

Published in:

SICE, 2007 Annual Conference

Date of Conference:

17-20 Sept. 2007