Efficient Online Globalized Dual Heuristic Programming With an Associated Dual Network
Ye Zhou (TU Delft - Control & Simulation, Universiti Sains Malaysia)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Globalized dual heuristic programming (GDHP) is the most comprehensive adaptive critic design, which employs its critic to minimize the error with respect to both the cost-to-go and its derivatives simultaneously. Its implementation, however, confronts a dilemma of either introducing more computational load by explicitly calculating the second partial derivative term or sacrificing the accuracy by loosening the association between the cost-to-go and its derivatives. This article aims at increasing the online learning efficiency of GDHP while retaining its analytical accuracy by introducing a novel GDHP design based on a critic network and an associated dual network. This associated dual network is derived from the critic network explicitly and precisely, and its structure is in the same level of complexity as dual heuristic programming critics. Three simulation experiments are conducted to validate the learning ability, efficiency, and feasibility of the proposed GDHP critic design.