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


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

July 5, 2016
October 1, 2016
December 20, 2016
local Gaussian process, adaptive critic design, value function approximation, two-phase value iteration, reinforcement learning
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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