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:: Volume 12, Issue 2 (Journal of Control, V.12, N.2 Summer 2018) ::
JoC 2018, 12(2): 13-25 Back to browse issues page
Suboptimal Solution of Nonlinear Graphical Games Using Single Network Approximate Dynamic Programming
Majid Mazouchi 1, Mohammad Bagher Naghibi Sistani * 1, Seyed Kamal Hosseini Sani 1
1- Ferdowsi University of Mashhad
Abstract:   (489 Views)

In this paper, an online learning algorithm based on approximate dynamic programming is proposed to approximately solve the nonlinear continuous time differential graphical games with infinite horizon cost functions and known dynamics. In the proposed algorithm, every agent employs a critic neural network (NN) to approximate its optimal value and control policy and utilizes the proposed weight tuning laws to learn its critic NN optimal weights in an online fashion. Critic NN weight tuning laws containing a stabilizer switch guarantees the closed-loop system stability and the control policies convergence to the Nash equilibrium. In this algorithm, there is no requirement for any set of initial stabilizing control policies anymore. Furthermore, Lyapunov theory is employed to show uniform ultimate boundedness of the closedloop system. Finally, a simulation example is presented to illustrate the efficiency of the proposed algorithm. 
 

Keywords: Approximate Dynamic Programming, Neural Networks, Optimal Control, Reinforcement learning
Full-Text [PDF 457 kb]   (161 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/06/16 | Accepted: 2017/12/10 | Published: 2018/10/3
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Mazouchi M, Naghibi Sistani M B, Hosseini Sani S K. Suboptimal Solution of Nonlinear Graphical Games Using Single Network Approximate Dynamic Programming . JoC. 2018; 12 (2) :13-25
URL: http://joc.kntu.ac.ir/article-1-382-en.html


Volume 12, Issue 2 (Journal of Control, V.12, N.2 Summer 2018) Back to browse issues page
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