Title

Advanced Adaptive Critic Designs

Abstract

We present a unified approach to a family of Adaptive Critic Designs (ACDs). ACDs approximate dynamic programming for optimal control and decision making in noisy, nonlinear, or nonstationary environments. This family consists of Heuristic Dynamic Programming (HDP), Dual Heuristic Programming (DHP), and Globalized Dual Heuristic Programming (GDHP), as well as their Action-Dependent forms (the prefix AD denotes these)[1]. The most powerful of these designs reported previously is GDHP [2,3]. After pointing out problems of the simple ACDs, we describe advanced ACDs and introduce ADGDHP. We also propose a general training procedure for ACDs and discuss some important research issues.

Meeting Name

World Congress on Neural Networks, International Neural Networks Society Annual Meeting (WCNN'96) (1996: Sep. 15-18, San Diego, CA)

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

805826084

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1996 Lawrence Erlbaum Associates, All rights reserved.

This document is currently not available here.


Share

 
COinS