Variable resolution discretization in the joint space

Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04

Volumn , Issue , 2004, Pages 449-455

  Monson, Christopher K ^a   Wingate, David ^a   Seppi, Kevin D ^a   Peterson, Todd S ^b  

^a BRIGHAM YOUNG UNIVERSITY   (United States)

^b Utah Valley State College   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL PROBLEMS; JOINT SPACE TRIANGULATION LEARNER (JOSTLE); REINFORCEMENT LEARNING (RL); VARIABLE RESOLUTION DISCRETIZATION (VRD);

APPROXIMATION THEORY; DATA STRUCTURES; INTERPOLATION; LEARNING SYSTEMS; PIECEWISE LINEAR TECHNIQUES; PROBLEM SOLVING;

ALGORITHMS;

EID: 21244478586     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

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2. B. Friedland. In Advanced Control System Design, New Jersey, 1996. Prentice Hall.
3. G. J. Gordon. Stable function approximation in dynamic programming. In A. Prieditis and S. Russell, editors. Proceedings of the Twelfth International Conference on Machine Learning, pages 261-268. San Francisco, CA, 1995. Morgan Kaufmann.
4. L. P. Kaelbling, M. L. Littman, and A. P. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237-285, 1996.
5. C. K. Monson. Reinforcement learning in the joint space: Value iteration in worlds with continuous states and actions. Master's thesis, Brigham Young University, Computer Science Department, Apr. 2003.
6. R. Muños and A. Moore. Variable resolution discretization in optimal control. Machine Learning, 49, Numbers 2/3:291-323, November/December 2002.