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Proceedings Paper

Reinforcement learning for robot control
Author(s): William D. Smart; Leslie Pack Kaelbling
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Paper Abstract

Writing control code for mobile robots can be a very time-consuming process. Even for apparently simple tasks, it is often difficult to specify in detail how the robot should accomplish them. Robot control code is typically full of magic numbers that must be painstakingly set for each environment that the robot must operate in. The idea of having a robot learn how to accomplish a task, rather than being told explicitly is an appealing one. It seems easier and much more intuitive for the programmer to specify what the robot should be doing, and let it learn the fine details of how to do it. In this paper, we describe JAQL, a framework for efficient learning on mobile robots, and present the results of using it to learn control policies for simple tasks.

Paper Details

Date Published: 18 February 2002
PDF: 12 pages
Proc. SPIE 4573, Mobile Robots XVI, (18 February 2002); doi: 10.1117/12.457434
Show Author Affiliations
William D. Smart, Washington Univ. (United States)
Leslie Pack Kaelbling, MIT Artificial Intelligence Lab. (United States)

Published in SPIE Proceedings Vol. 4573:
Mobile Robots XVI
Douglas W. Gage; Howie M. Choset, Editor(s)

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