Value-Gradient Learning

Fairbank, M. & Alonso, E. (2012). Value-Gradient Learning. Paper presented at the WCCI 2012 IEEE World Congress on Computational Intelligence, 10-06-2012 - 15-06-2012, Brisbane, Australia.

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We describe an Adaptive Dynamic Programming algorithm VGL(λ) for learning a critic function over a large continuous state space. The algorithm, which requires a learned model of the environment, extends Dual Heuristic Dynamic Programming to include a bootstrapping parameter analogous to that used in the reinforcement learning algorithm TD(λ). We provide on-line and batch mode implementations of the algorithm, and summarise the theoretical relationships and motivations of using this method over its precursor algorithms Dual Heuristic Dynamic Programming and TD(λ). Experiments for control problems using a neural network and greedy policy are provided.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Value-Gradient Learning, Dual Heuristic Dynamic Programming, DHP, Adaptive Dynamic Programming
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Informatics > Department of Computing

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