Data-driven learning and control with multiple critic networks

Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Volumn , Issue , 2012, Pages 523-527

  He, Haibo ^a   Ni, Zhen ^a   Zhao, Dongbin ^b  

^a University of Rhode Island   (United States)

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

adaptive dynamic programming (ADP); external reinforcement signal; goal representation; hierarchical structure; internal reinforcement signal; multiple critic networks

Indexed keywords

ADAPTIVE DYNAMIC PROGRAMMING; GOAL REPRESENTATION; HIERARCHICAL STRUCTURES; MULTIPLE CRITIC; REINFORCEMENT SIGNAL;

BENCHMARKING; DYNAMIC PROGRAMMING; INTELLIGENT CONTROL; REINFORCEMENT;

E-LEARNING;

EID: 84872330793     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WCICA.2012.6357935     Document Type: Conference Paper

References (19)

1. H. He, Z. Ni, and J. Fu, "A three-network architecture for on-line learning and optimization based on adaptive dynamic programming," Neurocomputing, vol. 78, no. 1, pp. 3-13, 2012.
2. P. J. Werbos, "Using ADP to understand and replicate brain intelligence: the next level design," in IEEE Int. Symposium on Approximate Dynamic Programming and Reinforcement Learning, pp. 209-216, 2007.
3. P. J. Werbos, "Intelligence in the brain: A theory of how it works and how to build it," Neural Networks, pp. 200-212, 2009.
4. J. Si, A. G. Barto, W. B. Powell, and D. C. Wunsch, Handbook of Learning and Approximate Dynamic Programming. IEEE Press, 2004.
5. F. Y. Wang, N. Jin, D. Liu, and Q. Wei, "Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ε-error bound," IEEE Transactions on Neural Networks, vol. 22, no. 1, pp. 24-36, 2011.
6. J. Fu, H. He, and X. Zhou, "Adaptive learning and control for mimo system based on adaptive dynamic programming," IEEE Transactions on Neural Networks, vol. 22, no. 7, pp. 1133-1148, 2011.
7. S. Ray, G. K. Venayagamoorthy, B. Chaudhuri, and R. Majumder, "Comparison of adaptive critics and classical approaches based wide area controllers for a power system," IEEE Trans. on Syst. Man, Cybern., Part B, vol. 38, no. 4, pp. 1002-1007, 2008.
8. D. V. Prokhorov and D. C. Wunsch, "Adaptive critic designs," IEEE Trans. on Neural Netw., vol. 8, no. 5, pp. 997-1007, 1997.
9. P. J. Werbos, Handbook of Intelligent Control, ch. Neuralcontrol and Supervised Learning: an Overview and Evaluation. New York: Van Nostrand, 1992.
10. P. H. Eaton, and D. V. Prokhorov, and D. C. Wunsch II., "Neurocontroller alternatives for fuzzy ball-and-beam systems with nonuniform nonlinear friction," IEEE Trans. Neural Netw., vol. 11, no. 2, pp. 423-435, 2000.
11. J. Si and Y. T. Wang, "On-line learning control by association and reinforcement," IEEE Trans. on Neural Netw., vol. 12, no. 2, pp. 264-276, 2001.
12. J. Si, L. Yang, and D. Liu, Handbook of Learning and Approximate Dynamic Programming, ch. Direct Neural Dynamic Programming, pp. 125-151. IEEE Press, 2004.
13. L. Yang, J. Si, K. S. Tsakalis, and A. A. Rodriguez, "Direct heuristic dynamic programming for nonlinear tracking conrol with filtered tracking error," IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, vol. 39, no. 6, pp. 1617-1622, 2009.
14. H. G. Zhang, Q. L. Wei, and Y. H. Luo, "A novel infinite-time optimal tracking control scheme for a class of discrete-time nonlinear systems via the greedy hdp iteration algorithm," IEEE Transactions on System, Man and Cybernetics, Part B, vol. 38, no. 4, pp. 937-942, 2008.
15. H. G. Zhang, Y. H. Luo, and D. Liu, "Neural-network-based nearoptimal control for a class of discrete-time affine nonlinear systems with control constraints," IEEE Transactions on Neural Networks, vol. 20, no. 9, pp. 1490-1503, 2009.
16. H. G. Zhang, Q. L. Wei, and D. Liu, "An iterative approximate dynamic programming method to solve for a class of nonlinear zerosum differential games," Automatica, vol. 47, no. 1, pp. 207-214, 2011.
17. P. J. Werbos, "Backpropagation through time: What it does and how to do it," in Proc. IEEE, vol. 78, pp. 1550-1560, 1990.
18. P. J. Werbos, The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting. Wiley-Interscience, 1994.
19. Z. Ni, H. He, D. Zhao, and D. V. Prokhorov, "Reinforcement learning control based on multi-goal representation using hierarchical heuristic dynamic programming," Proc. Int. Joint Conf. Neural Networks (IJCNN), 2012 (in press).