Learning curve bounds for a Markov decision process with undiscounted rewards

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Learning curve bounds for a Markov decision process with undiscounted rewards

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Annual Workshop on Computational Learning Theory archive
Proceedings of the ninth annual conference on Computational learning theory table of contents

Desenzano del Garda, Italy

Pages: 147 - 156

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

Lawrence K. Saul	Center for Biological and Computational Learning, Massachusetts Institute of Technology, 79 Amherst Street, E10-243, Cambridge, MA
Satinder P. Singh	Harlequin Inc., One Cambridge Center, Cambridge, MA and Center for Biological and Computational Learning, Massachusetts Institute of Technology, 79 Amherst Street, E10-243, Cambridge, MA

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 13, Citation Count: 2

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Andrew G. Barto , Steven J. Bradtke , Satinder P. Singh, Learning to act using real-time dynamic programming, Artificial Intelligence, v.72 n.1-2, p.81-138, Jan. 1995 [doi>10.1016/0004-3702(94)00011-O]
	2	Dimitri P. Bertsekas, Dynamic Programming and Optimal Control, Two Volume Set, Athena Scientific, 1995
	3	J. A. Bucklew. Large deviatwn techniques zn deciszon, simulation, and est~matzon. John Wiley & Sons, New York, 1990.
	4	Claude-Nicolas Fiechter, Efficient reinforcement learning, Proceedings of the seventh annual conference on Computational learning theory, p.88-97, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181019]
	5	David Haussler , H. Sebastian Seung , Michael Kearns , Naftali Tishby, Rigorous learning curve bounds from statistical mechanics, Proceedings of the seventh annual conference on Computational learning theory, p.76-87, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181018]
	6	K. Huang. Statistical Mechanics. John Wiley &; Sons, New York, NY, 1987.
	7	M. Marcus and H. Mine. A survey of matrix theory and matrix inequalities. Dover, New York, 1992.
	8	Lawrence K. Saul , Satinder P. Singh, Markov decision processes in large state spaces, Proceedings of the eighth annual conference on Computational learning theory, p.281-288, July 05-08, 1995, Santa Cruz, California, United States [doi>10.1145/225298.225332]
	9	H. S. Seung, H. Sompolinsky, and N. Tishby. Statistical mechanics of learning from examples. Physical Review A 45: 6056-6091, 1992.
	10	Satinder P. Singh, Reinforcement learning algorithms for average-payoff Markovian decision processes, Proceedings of the twelfth national conference on Artificial intelligence (vol. 1), p.700-705, October 1994, Seattle, Washington, United States
	11	Richard S. Sutton, Learning to Predict by the Methods of Temporal Differences, Machine Learning, v.3 n.1, p.9-44, August 1988 [doi>10.1023/A:1022633531479]
	12	T. Watkin, A. Rau, and 5I. Biehl. The statistical mechanics of learning a rule. Reviews of Modern Physzcs 65:499-556, 1993.
	13	Christopher J. C. H. Watkins , Peter Dayan, Technical Note: $\cal Q$ -Learning, Machine Learning, v.8 n.3-4, p.279-292, May 1992 [doi>10.1007/BF00992698]

CITED BY 2

	Satinder Singh , Peter Dayan, Analytical Mean Squared Error Curves for Temporal DifferenceLearning, Machine Learning, v.32 n.1, p.5-40, July 1998
	Michael Kearns , Satinder Singh, Near-Optimal Reinforcement Learning in Polynomial Time, Machine Learning, v.49 n.2-3, p.209-232, November-December 2002