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Direct Neural Dynamic Programming

  • Lei Yang
  • Russell Enns
  • Yu-Tsung Wang
  • Jennie Si
Part of the Control Engineering book series (CONTRENGIN)

Abstract

This chapter is about approximate dynamic programming (ADP), which has been referred to by many different names, such as “reinforcement learning,” “adaptive critics,” “neuro-dynamic programming,” and “adaptive dynamic programming.” The fundamental issue under consideration is optimization over time by using learning and approximation to handle problems that severely challenge conventional methods due to their very large scale and/or lack of sufficient prior knowledge. In this chapter we discuss the relationships, results, and challenges of various approaches under the theme of ADP. We also introduce the fundamental principles of our direct neural dynamic programming (NDP). We demonstrate its application for a continuous state control problem using an industrial scale Apache helicopter model. This is probably one of the first studies where an ADP type of algorithm has been applied to a complex, realistic, continuous state problem, which is a major challenge in machine learning when dealing with scalability or generalization.

Keywords

Artificial Neural Network Optimal Policy Reinforcement Learning Action Network Main Rotor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Lei Yang
  • Russell Enns
  • Yu-Tsung Wang
  • Jennie Si

There are no affiliations available

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