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JACIII Vol.20 No.7 pp. 1135-1140
doi: 10.20965/jaciii.2016.p1135
(2016)

Paper:

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Adaptive Critic Design with Local Gaussian Process Models

Wei Wang, Xin Chen†, and Jianxin He

School of Automation, China University of Geosciences
Wuhan 430074, China

†Corresponding author

Received:
July 5, 2016
Accepted:
October 1, 2016
Published:
December 20, 2016
Creative Commons License
Keywords:
local Gaussian process, adaptive critic design, value function approximation, two-phase value iteration, reinforcement learning
Abstract
In this paper, local Gaussian process (GP) approximation is introduced to build the critic network of adaptive dynamic programming (ADP). The sample data are partitioned into local regions, and for each region, an individual GP model is utilized. The nearest local model is used to predict a given state-action point. With the two-phase value iteration method for a Gaussian-kernel (GK)-based critic network which realizes the update of the hyper-parameters and value functions simultaneously, fast value function approximation can be achieved. Combining this critic network with an actor network, we present a local GK-based ADP approach. Simulations were carried out to demonstrate the feasibility of the proposed approach.
Cite this article as:
W. Wang, X. Chen, and J. He, “Adaptive Critic Design with Local Gaussian Process Models,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.7, pp. 1135-1140, 2016.
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