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A Reinforcement Learning Neural Network for Robotic Manipulator Control. | LitMetric

A Reinforcement Learning Neural Network for Robotic Manipulator Control.

Neural Comput

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, Shenyang, P.R.C.

Published: July 2018

AI Article Synopsis

Article Abstract

We propose a neural network model for reinforcement learning to control a robotic manipulator with unknown parameters and dead zones. The model is composed of three networks. The state of the robotic manipulator is predicted by the state network of the model, the action policy is learned by the action network, and the performance index of the action policy is estimated by a critic network. The three networks work together to optimize the performance index based on the reinforcement learning control scheme. The convergence of the learning methods is analyzed. Application of the proposed model on a simulated two-link robotic manipulator demonstrates the effectiveness and the stability of the model.

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Source
http://dx.doi.org/10.1162/neco_a_01079DOI Listing

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