Learning to act using real-time dynamic-programming

Learning to act using real-time dynamic-programming

0.00 Avg rating0 Votes
Article ID: iaor19971473
Country: United States
Volume: 72
Issue: 1/2
Start Page Number: 81
End Page Number: 138
Publication Date: Jan 1995
Journal: Artificial Intelligence
Authors: , ,
Keywords: programming: dynamic, control
Abstract:

Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning results into reactive strategies for real-time control, as well as for learning such strategies when the system being controlled is incompletely known. The authors introduce an algorithm based on DP, which we call Real-Time DP (RTDP), by which an embedded system can improve its performance with experience, RTDP generalizes Korf’s Learning-Real-Time-AX algorithm to problems involving uncertainty. They invoke results from the theory of asynchronous DP to prove that RTDP achieves optimal behavior in several different classes of problems. The authors also use the theory of asynchronous DP to illuminate aspects of other DP-based reinforcement learning methods such as Watkins’ Q-learning algorithm. A secondary aim of this article is to provide a bridge between AI research on real-time planning and learning and relevant concepts and algorithms from control theory.

Reviews

Required fields are marked *. Your email address will not be published.