Memory-Based Learning for Control.
Abstract
The central thesis of this article is that memory-based methods provide natural powerful mechanisms for high-anatomy learning control. This paper takes the form of a survey of the ways in which memory-based methods can and have been applied to control tasks, with an emphasis on tasks in robotics and manufacturing. We explain the various forms that control tasks can take, and how this impacts on the choice of learning algorithm. We show a progression of five increasingly more complex algorithms which are applicable to increasingly more complex kinds of control tasks. We examine their empirical behavior on robotic and industrial tasks. The final section discusses the interesting impact that explicitly remembering all previous experiences has on the problem of learning control.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 1995
- Accession Number
- ADA311505
Entities
People
- A. W. Moore
- C. G. Atkeson
- S. A. Schaal
Organizations
- Carnegie Mellon University