Journal
NAVAL RESEARCH LOGISTICS
Volume 56, Issue 3, Pages 239-249Publisher
WILEY
DOI: 10.1002/nav.20347
Keywords
approximate dynamic programming; reinforcement learning; neuro-dynamic programming; stochastic optimization; Monte Carlo simulation
Categories
Funding
- Air Force Office of Scientific Research [AFOSR-F49620-93-1-0098]
Ask authors/readers for more resources
Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well-known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. (C) 2009 Wiley Periodicals, Inc. Naval Research Logistics 56: 239-249,2009
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available