Research Repository

An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time

Fairbank, M and Alonso, E and Prokhorov, D (2013) 'An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time.' IEEE Transactions on Neural Networks and Learning Systems, 24 (12). pp. 2088-2100. ISSN 2162-237X

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We consider the adaptive dynamic programming technique called Dual Heuristic Programming (DHP), which is designed to learn a critic function, when using learned model functions of the environment. DHP is designed for optimizing control problems in large and continuous state spaces. We extend DHP into a new algorithm that we call Value-Gradient Learning, VGL(?), and prove equivalence of an instance of the new algorithm to Backpropagation Through Time for Control with a greedy policy. Not only does this equivalence provide a link between these two different approaches, but it also enables our variant of DHP to have guaranteed convergence, under certain smoothness conditions and a greedy policy, when using a general smooth nonlinear function approximator for the critic. We consider several experimental scenarios including some that prove divergence of DHP under a greedy policy, which contrasts against our proven-convergent algorithm.

Item Type: Article
Uncontrolled Keywords: Adaptive Dynamic Programming; Dual Heuristic Programming; Value-Gradient Learning; Backpropagation Through Time; Neural Networks
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 05 Aug 2016 14:04
Last Modified: 23 Sep 2022 19:04

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