Efficient Memory-Based Dynamic Programming

Proceedings of the 12th International Conference on Machine Learning, ICML 1995

Volumn , Issue , 1995, Pages 438-446

  Peng, Jing ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION SPACES; CONTINUOUS ACTIONS; CONTINUOUS STATE SPACE; GENERALISATION; LOCALLY WEIGHTED REGRESSION; MEMORY-BASED APPROACH; NEW APPROACHES; QUANTISATION; STATE-SPACE; VARIABLE RESOLUTION;

EID: 2342514644     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Aha, D. W. , Kibler, D. k Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning 6:37-66.
2. Aha, D. W. k Goldstone, R. L. (1990). Learning attribute relevance in context in instance-based learning algorithms. Proceedings of the 12th Annual Conference of the Cognitive Science Society, 141-148.
3. Albus, J. S. (1975a). Data storage in the cerebellar model articulation controller (CMAC). Journal of Dynamic Systems, Measurement, and Control September:228-233.
4. Albus, J. S. (1975b). A new approach to manipulator control: The cerebellar model articulation controller (CM AC). Journal of Dynamic Systems, Measurement, and Control September:220-227. Atkeson, C. G. (1991). Memory-based learning control.
5. Proceedings of American Control Conference, 2131-2136.
6. Barnhill, R. E. (1977). Representation and approximation of surfaces. Mathematical Software 111:69-120.
7. Barto, A. G., Sutton, R. S. k Anderson, C. W. (1983). Neuronlike elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics 13:835-846.
8. Chow, C. S. k Tsitsiklis, J. N. (1991). An optimal one-way multigrid algorithm for discrete-time stochastic control. IEEE Transactions on Automatic Control 36(8):898-914.
9. Connell, M. E. k Utgoff, P. E. (1987). Learning to control a dynamic physical system. Proceedings of AAAI- 1987.
10. Friedman, J.H. (1994), Flexible metric nearest neighbor classification. Department of Statistics and Stanford Linear Accelerator Center, Stanford University, Stanford, CA 94305.
11. Hillis, D. (1985). The Connection Machine. Cambridge, MA: MIT Press.
12. Larson, R. E. (1967). A survey of dynamic programming computational procedures. IEEE Transactions on Automatic Control, December:767-774.
13. Lin, L. J. (1992). Reinforcement learning for robots using neural networks. Ph. D. Dissertation, Carnegie Mellon University, PA.
14. Lowe, D. G. (1995). Similarity Metric Learning for a Variable-Kernal Classifier. Neural Computation 7(l):72-85.
15. Luus, R. (1992). On the application of iterative dynamic programming to singular optimal control. IEEE Transactions on Automatic Control, VOL. 37 NO. 11, 1802-1806.
16. Miller, W. T. Ill, Glanz, F. H. k Kraft, L. G. III (1987). Application of a general learning algorithm to the control of robotic manipulators. The International Journal of Robotics Research 6(2):84-98.
17. Miller, S. k Williams, R. J. (1992). Learning to control a bioreactor using a neural net Dyna-Q system. Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems, May.
18. Moore, A. W. (1991). Variable resolution dynamic programming: Efficiently learning action maps in multivariate real-valued state-spaces. Proceedings of the 8th International Machine Learning Workshop.
19. Moore, A. W. (1990a). Acquisition of dynamic control knowledge for a robotic manipulator. Proceedings of the 7th International Conference on Machine Learning, 244-252.
20. Moore, A. W. (1990b). Efficient memory-based learning for robot control. Ph. D Dissertation, University of Cambridge, UK.
21. Omohundro, S. M. (1987). Efficient algorithms with neural network behavior. Complex Systems 1:273-347.
22. Peng, P. (1993). Efficient Dynamic Programming- Based Learning for Control. Ph. D Dissertation, Northeastern University, MA.
23. Peng, J. k Williams, R. J. (1993). Efficient learning and planning within the Dyna framework. Adaptive Behavior Vol. I No. 4•
24. Rumelhart, D. E., Hinton, G. E. k Williams, R. J. (1986). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructures of Cognition 1 (Rumelhart, D. E. k McClelland, J. L. Eds), Chapter 8.
25. Salzberg, S. (1991). A Nearest Hyperrectangle Learning Method. Machine Learning 6:251-276.
26. Schumaker, L. L. (1976). Fitting surfaces to scattered data. Approximation Theory II. Academic Press, 203- 268.
27. Schwartz, D. B. (1993). ATM scheduling with queuing delay predictions. Proceedings of the Tenth International Conference on Machine Learning, 306-313.
28. Stanfill, C. k Waltz, D. (1986). Towards memory-based reasoning. Communications of ACM 29(12):1213-1228.
29. Sutton, R. S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. Proceedings of the Seventh International Conference on Machine Learning, 216- 224.
30. Volper, D. J. k Hampson, S. E. (1987). Learning and using specific instances. Biological Cybernetics 57:57- 71.
31. Watkins, C. J. C. H. (1989). Learning from delayed rewards. Ph.D. Dissertation, King's College, UK.