-
1
-
-
85151728371
-
Residual algorithms: Reinforcement learning with function approximation
-
Morgan Kaufman Publishers, San Francisco, CA., July
-
L. C. Baird. Residual algorithms: Reinforcement learning with function approximation. In Proceedings of the Twelfth International Conference on Machine Learning, pages 30-77. Morgan Kaufman Publishers, San Francisco, CA., July 1995.
-
(1995)
Proceedings of the Twelfth International Conference on Machine Learning
, pp. 30-77
-
-
Baird, L.C.1
-
3
-
-
85153940465
-
Generalization in reinforcement learning: Safely approximating the value function
-
G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Cambridge, MA. The MIT Press
-
J. A. Boyan and A. W. Moore. Generalization in reinforcement learning: Safely approximating the value function. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7, pages 369-376, Cambridge, MA, 1995. The MIT Press.
-
(1995)
Advances in Neural Information Processing Systems
, vol.7
, pp. 369-376
-
-
Boyan, J.A.1
Moore, A.W.2
-
4
-
-
33645283722
-
-
L. Bull and T. Kovacs, editors. Foundations of Learning Classifier Systems. Springer
-
L. Bull and T. Kovacs, editors. Foundations of Learning Classifier Systems, volume 183 of Studies in Fuzziness and Soft Computing. Springer, 2005.
-
(2005)
Studies in Fuzziness and Soft Computing
, vol.183
-
-
-
5
-
-
34249944702
-
Gradient descent methods in learning classifier systems
-
Illinois Genetic Algorithms Laboratory - University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Avenue, Urbana, IL 61801, Jan.
-
M. Butz, D. G. Goldberg, and P. L. Lanzi. Gradient descent methods in learning classifier systems. Technical Report 2003028, Illinois Genetic Algorithms Laboratory - University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Avenue, Urbana, IL 61801, Jan. 2003.
-
(2003)
Technical Report 2003028
-
-
Butz, M.1
Goldberg, D.G.2
Lanzi, P.L.3
-
6
-
-
27344437190
-
Gradient descent methods in learning classifier systems: Improving xcs performance in multistep problems
-
Oct.
-
M. V. Butz, D. E. Goldberg, and P. L. Lanzi. Gradient descent methods in learning classifier systems: Improving xcs performance in multistep problems. IEEE Transaction on Evolutionary Computation, 9(5):452-473, Oct. 2005.
-
(2005)
IEEE Transaction on Evolutionary Computation
, vol.9
, Issue.5
, pp. 452-473
-
-
Butz, M.V.1
Goldberg, D.E.2
Lanzi, P.L.3
-
7
-
-
26944470971
-
An algorithmic description of xcs
-
M. V. Butz and S. W. Wilson. An algorithmic description of xcs. Journal of Soft Computing, 6(3-4): 144-153, 2002.
-
(2002)
Journal of Soft Computing
, vol.6
, Issue.3-4
, pp. 144-153
-
-
Butz, M.V.1
Wilson, S.W.2
-
10
-
-
0043144286
-
An analysis of generalization in the XCS classifier system
-
P. L. Lanzi. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation Journal, 7(2):125-149, 1999.
-
(1999)
Evolutionary Computation Journal
, vol.7
, Issue.2
, pp. 125-149
-
-
Lanzi, P.L.1
-
12
-
-
27144463204
-
Generalization in the xcsf classifier system: Analysis, improvement, and extension
-
Illinois Genetic Algorithms Laboratory - University of Illinois at Urbana-Champaign
-
P. L. Lanzi, D. Loiacono, S. W. Wilson, and D. E. Goldberg. Generalization in the xcsf classifier system: Analysis, improvement, and extension. Technical Report 2005012, Illinois Genetic Algorithms Laboratory - University of Illinois at Urbana-Champaign, 2005.
-
(2005)
Technical Report 2005012
-
-
Lanzi, P.L.1
Loiacono, D.2
Wilson, S.W.3
Goldberg, D.E.4
-
13
-
-
0036832956
-
Kernel-based reinforcement learning
-
D. Ormoneit and S. Sen. Kernel-based reinforcement learning. Machine Learning, 49(2-3):161-178, 2002.
-
(2002)
Machine Learning
, vol.49
, Issue.2-3
, pp. 161-178
-
-
Ormoneit, D.1
Sen, S.2
-
14
-
-
13244294436
-
-
PhD thesis, School of Computer Science. The University of Birmingham, Birmingham, B15 2TT, Dec.
-
S. I. Reynolds. Reinforcement Learning with Exploration. PhD thesis, School of Computer Science. The University of Birmingham, Birmingham, B15 2TT, Dec. 2002.
-
(2002)
Reinforcement Learning with Exploration
-
-
Reynolds, S.I.1
-
15
-
-
85156221438
-
Generalization in reinforcement learning: Successful examples using sparse coarse coding
-
D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors. The MIT Press, Cambridge, MA.
-
R. S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 8, pages 1038-1044. The MIT Press, Cambridge, MA., 1996.
-
(1996)
Advances in Neural Information Processing Systems
, vol.8
, pp. 1038-1044
-
-
Sutton, R.S.1
-
17
-
-
4644328593
-
Off-policy temporal-difference learning with function approximation
-
Morgan Kaufmann, San Francisco, CA, 2001
-
R. S. Sutton, D. Precup, and S. Dasgupta. Off-policy temporal-difference learning with function approximation. In Proceedings of the 18th International Conference on Machine Learning., pages 417-424. Morgan Kaufmann, San Francisco, CA, 2001.
-
Proceedings of the 18th International Conference on Machine Learning
, pp. 417-424
-
-
Sutton, R.S.1
Precup, D.2
Dasgupta, S.3
-
18
-
-
0031143730
-
An analysis of temporal-difference learning with function approximation
-
May
-
J. N. Tsitsiklis and B. V. Roy. An analysis of temporal-difference learning with function approximation. IEEE Transactions on Automatic Control, 42 (5)-.674-690, May 1997.
-
(1997)
IEEE Transactions on Automatic Control
, vol.42
, Issue.5
, pp. 674-690
-
-
Tsitsiklis, J.N.1
Roy, B.V.2
-
22
-
-
27144534351
-
-
chapter Neurocomputing: Foundation of Research. The MIT Press, Cambridge
-
B. Widrow and M. E. Hoff. Adaptive Switching Circuits, chapter Neurocomputing: Foundation of Research, pages 126-134. The MIT Press, Cambridge, 1988.
-
(1988)
Adaptive Switching Circuits
, pp. 126-134
-
-
Widrow, B.1
Hoff, M.E.2
-
24
-
-
22944460232
-
Convergence and divergence in standard and averaging reinforcement learning
-
Springer-Verlag, Berlin, Heidelberg
-
M. Wiering. Convergence and divergence in standard and averaging reinforcement learning. In Proceedings of the 15th European Conference on Machine Learning, volume 3201, pages 477-488. Springer-Verlag, Berlin, Heidelberg, 2004.
-
(2004)
Proceedings of the 15th European Conference on Machine Learning
, vol.3201
, pp. 477-488
-
-
Wiering, M.1
-
25
-
-
0001387704
-
Classifier fitness based on accuracy
-
S. W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149-175, 1995. http://prediction-dynamics.com/.
-
(1995)
Evolutionary Computation
, vol.3
, Issue.2
, pp. 149-175
-
-
Wilson, S.W.1
-
27
-
-
27144549349
-
Classifiers that approximate functions
-
S. W. Wilson. Classifiers that approximate functions. Journal of Natural Computating, 1(2-3):211-234, 2002.
-
(2002)
Journal of Natural Computating
, vol.1
, Issue.2-3
, pp. 211-234
-
-
Wilson, S.W.1
|