Approximate dynamic programming using support vector regression

Proceedings of the IEEE Conference on Decision and Control

Volumn , Issue , 2008, Pages 3811-3816

  Bethke, Brett ^a   How, Jonathan P ^b   Ozdaglar, Asuman ^c  

^a Massachusetts Institute of Technology Cambridge   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c USA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; MEMORY ARCHITECTURE; SUPPORT VECTOR REGRESSION;

APPROXIMATE DYNAMIC PROGRAMMING; APPROXIMATION ARCHITECTURE; COMPUTATIONAL RESULTS; COMPUTATIONALLY EFFICIENT; POLICY ITERATION; POLICY ITERATION ALGORITHMS; SUPPORT VECTOR REGRESSION (SVR); TRAJECTORY SIMULATION;

DYNAMIC PROGRAMMING;

EID: 62949146458     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2008.4739322     Document Type: Conference Paper

References (18)

1. D. Bertsekas, Dynamic Programming and Optimal Control. Belmont, MA: Athena Scientific, 2007.
2. D. Bertsekas, J. Tsitsiklis, Neuro Dynamic Programming. Belmont, MA: Athena Scientific, 1996.
3. G. Tesauro, "Temporal difference learning and TD Gammon," Com mun. ACM, vol. 38, no. 3, pp. 58-68, 1995.
4. M. Lagoudakis and R. Parr, "Least squares policy iteration," Journal of Machine Learning Research, vol. 4, pp. 1107-1149, 2003.
5. A. B. W. P. J. Si and D. Wunsch, Learning and Approximate Dynamic Programming. NY: IEEE Press, 2004. [Online]. Available: http://citeseer.ist. psu.edu/651143.html
6. D. Bertsekas and S. Ioffe, "Temporal Differences Based Pol icy Iteration and Applications in Neuro Dynamic Programming," http://web.mit.edu/people/dimitrib/Tempdif.pdf, 1996.
7. M. Valenti, "Approximate Dynamic Programming with Applications in Multi Agent Systems," Ph. D. dissertation, Massachusetts Institute of Technology, 2007.
8. A. Smola, B. Scholkopf, "A Tutorial on Support Vector Regression," Statistics and Computing, vol. 14, pp. 199-222, 2004.
9. B. Curry and P. Morgan, "Model selection in neural networks: Some difficulties," European Journal of Operational Research, vol. Volume 170, Issue 2, pp. p. 567-577, 2006.
10. R. Patrascu, "Linear approximations for factored markov decision processes," PhD Dissertation, University of Waterloo, Department of Computer Science, February 2004.
11. A. S. B. Scholkopf, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, 2002.
12. C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, 2006.
13. J. Platt, "Using sparseness and analytic QP to speed training of support vector machines," in Advances in Neural Information Processing Systems, 1999, pp. 557-563.
14. S. Keerthi, S. Shevade, C. Bhattacharyya, and K. Murthy, "Im provements to Platt's SMO algorithm for SVM classifier design," http://citeseer.ist.psu.edu/244558.html, 1999.
15. C. C. Chang and C. J. Lin, LIBSVM: a library for support vector machines, 2001, Software available at http://www.csie.ntu.edu.tw/cjlin/ libsvm.
16. R. S. Sutton, "Learning to Predict by the Methods of Temporal Differ ences," Machine Learning, vol. 3, pp. 9-44, 1988.
17. R. Sutton, A. Barto, Reinforcement learning: An introduction. MIT Press, 1998.
18. C. Rasmussen and M. Kuss, "Gaussian processes in reinforce ment learning," Advances in Neural Information Processing Systems, vol. 16, pp. 751-759, 2004.