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Reinforcement learning and mistake bounded algorithms

Published: 06 July 1999 Publication History
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References

[1]
Peter Auer, Nicolb Cesa-Bianchi, Yoav Freund, and Robert E. Schapire. Gambling in a rigged casino: The adversarial multiarmed bandit problem. In 36th Annual Symposium on Foundations of Computer Science, pages 322-331, 1995.
[2]
N. Alon and J. Spencer. The Probabilistic Method. Wiley, 1992.
[3]
Richard Bellman. Dynamic Programming. Princeton University Press, Princeton, N.J., 1957.
[4]
Dimitri P. Bertsekas. Dynamic Programming: deterministic and stochastic methods. Prentice-Hall, 1987.
[5]
Dimitri P. Bertsekas. Dynamic Programming and optimal control. Athena Scientific, 1995.
[6]
Dimitri P. Bertsekas and John N. Tsitsiklis. Neuro-Dynamic programing. Athena Scientific, 1996. Deals with various techniques related to Neural networks.
[7]
Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest. Introduction to Algorithms. MIT Press, 1990.
[8]
R. Howard. Dynamic Programming and Markov Processes. MIT Press, 1960.
[9]
M. Kearns, Y. Mansour, and A. Ng. Approximate planning in large pomdps via reusable trajectories.
[10]
Michael J. Kearns and Umesh V. Vaxirani. Computational Learning Theory. MIT press, 1994.
[11]
Nick Littlestone. Learning when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285-318, 1988.
[12]
Michael L. Littman. Algorithms for sequential decision making. PhD thesis, Brown University, 1996.
[13]
Christos H. Papadimitrio and John N. Tsitsiklis. The complexity of markov decision processes. Mathematics of Operation Research, 12(3):441-450, 1987.
[14]
Richard S. Sutton and Andrew G. Barto. Reinforcement Learning. MIT press, 1998.
[15]
Gerald Tesauro. TD-Gammon, A Self- Teaching Backgammon Program, Achieves Master-Level Play. Neural Computation, 6:215-219, 1994.
[16]
V.N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer-Verlag, New York, 1982.

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  • (2016)PAC reinforcement learning with rich observationsProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157096.3157303(1848-1856)Online publication date: 5-Dec-2016

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    cover image ACM Conferences
    COLT '99: Proceedings of the twelfth annual conference on Computational learning theory
    July 1999
    333 pages
    ISBN:1581131674
    DOI:10.1145/307400
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    Published: 06 July 1999

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    • (2016)PAC reinforcement learning with rich observationsProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157096.3157303(1848-1856)Online publication date: 5-Dec-2016

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