Tree-based variable selection for dimensionality reduction of large-scale control systems

IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

Volumn , Issue , 2011, Pages 62-69

  Castelletti, Andrea ^a   Galelli, Stefano ^a   Restelli, Marcello ^a   Soncini Sessa, Rodolfo ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL POLICY; CONTROL PROBLEMS; CONTROL PURPOSE; DATA SETS; DIMENSIONALITY REDUCTION; DISTURBANCE VARIABLES; HIGH-DIMENSIONAL; MODEL FREE; NON-LINEAR; POOR PERFORMANCE; PROBLEM DIMENSIONALITY; RANKING ALGORITHM; STATE TRANSITIONS; STATE VARIABLES; STATISTICAL MEASURES; TRANSITION MODEL; TREE-BASED; VARIABLE SELECTION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DYNAMIC PROGRAMMING; REAL VARIABLES; REINFORCEMENT LEARNING;

ADAPTIVE CONTROL SYSTEMS;

EID: 80052251399     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ADPRL.2011.5967387     Document Type: Conference Paper

References (22)

1. E. Van Nes and M. Scheffer, "A strategy to improve the contribution of complex simulation models to ecological theory," Ecological modelling, vol. 185, no. 2-4, pp. 153-164, 2005. (Pubitemid 40564496)
2. L. Li, T. Walsh, and M. Littman, "Towards a unified theory of state abstraction for MDPs," in Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics, 2006, pp. 531-539.
3. T. Dean and R. Givan, "Model minimization in Markov decision processes," in Proceedings of AAAI, 1997, pp. 106-111.
4. B. Ravindran and A. Barto, "Model minimization in hierarchical reinforcement learning," Abstraction, Reformulation, and Approximation, pp. 196-211, 2002.
5. R. Givan, T. Dean, and M. Greig, "Equivalence notions and model minimization in Markov decision processes," Artificial Intelligence, vol. 147, no. 1-2, pp. 163-223, 2003.
6. C. Boutilier, R. Dearden, and M. Goldszmidt, "Exploiting structure in policy construction," in Proceedings of IJCAI, vol. 14, 1995, pp. 1104-1113.
7. C. Guestrin, D. Koller, R. Parr, and S. Venkataraman, "Efficient solution algorithms for factored MDPs," Journal of Artificial Intelligence Research, vol. 19, no. 1, pp. 399-468, 2003. (Pubitemid 41525926)
8. T. Dean, R. Givan, and K. Kim, "Solving stochastic planning problems with large state and action spaces," in Proc. Fourth International Conference on Artificial Intelligence Planning Systems, 1998.
9. T. Degris, O. Sigaud, and P. Wuillemin, "Learning the structure of factored markov decision processes in reinforcement learning problems," in Proceedings of ICML. ACM, 2006, pp. 257-264.
10. A. Strehl, C. Diuk, and M. Littman, "Efficient structure learning in factored-state MDPs," in Proceedings of AAAI, vol. 22, no. 1, 2007, p. 645.
11. C. Vigorito and A. Barto, "Incremental structure learning in factored mdps with continuous states and actions," University of Massachusetts Amherst-Department of Computer Science, Tech. Rep, 2009.
12. B. Kveton, M. Hauskrecht, and C. Guestrin, "Solving factored MDPs with hybrid state and action variables," Journal of Artificial Intelligence Research, vol. 27, no. 1, pp. 153-201, 2006. (Pubitemid 44681376)
13. L. Wehenkel, Automatic learning techniques in power systems. Kluwer Academic Publishers, 1998.
14. R. E. Bellman, Dynamic Programming. Princeton, New Jersey, USA: Princeton University Press, 1957.
15. M. Puterman, Markov Decision Problems. New York: Wiley, 1994.
16. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, B. Book, Ed. Cambridge, MA: MIT Press, 1998.
17. P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine Learning, vol. 63, no. 1, pp. 3-42, 2006.
18. L. Breiman, Classification and regression trees. Chapman & Hall/CRC, 1984.
19. R. Fonteneau, L. Wehenkel, and D. Ernst, "Variable selection for dynamic treatment regimes: a reinforcement learning approach," in Proceedings of EWRL, 2008.
20. D. Ernst, P. Geurts, and L. Wehenkel, "Tree-based batch mode reinforcement learning," Journal of Machine Learning Research, vol. 6, no. 1, pp. 503-556, Apr. 2005.
21. A. Bonarini, C. Caccia, A. Lazaric, and M. Restelli, "Batch reinforcement learning for controlling a mobile wheeled pendulum robot," in Proceedings of IFIP AI, vol. 276. Springer Verlag, 2008, pp. 151-160.
22. A. Imerito, "Dynamic REservoir Simulation Model: Dyresm Science Manual," Centre for water Research - University of Western Australia, Crawley - Western Australia, CWR Tech. Report, 2007.