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Bibliography

1
R. Huber.
Selektive visuelle Aufmerksamkeit: Untersuchungen zum Erlernen von Fokustrajektorien durch neuronale Netze, 1990.
Diplomarbeit, Institut für Informatik, Technische Universität München.

2
M. I. Jordan.
Supervised learning and systems with excess degrees of freedom.
Technical Report COINS TR 88-27, Massachusetts Institute of Technology, 1988.

3
T. Kohonen.
Self-Organization and Associative Memory.
Springer, second edition, 1988.

4
Y. LeCun.
Une procédure d'apprentissage pour réseau à seuil asymétrique.
Proceedings of Cognitiva 85, Paris, pages 599-604, 1985.

5
P. W. Munro.
A dual back-propagation scheme for scalar reinforcement learning.
Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, WA, pages 165-176, 1987.

6
Nguyen and B. Widrow.
The truck backer-upper: An example of self learning in neural networks.
In IEEE/INNS International Joint Conference on Neural Networks, Washington, D.C., volume 1, pages 357-364, 1989.

7
D. B. Parker.
Learning-logic.
Technical Report TR-47, Center for Comp. Research in Economics and Management Sci., MIT, 1985.

8
T. Robinson and F. Fallside.
Dynamic reinforcement driven error propagation networks with application to game playing.
In Proceedings of the 11th Conference of the Cognitive Science Society, Ann Arbor, pages 836-843, 1989.

9
D. E. Rumelhart, G. E. Hinton, and R. J. Williams.
Learning internal representations by error propagation.
In Parallel Distributed Processing, volume 1, pages 318-362. MIT Press, 1986.

10
J. H. Schmidhuber.
Learning algorithms for networks with internal and external feedback.
In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, editors, Proc. of the 1990 Connectionist Models Summer School, pages 52-61. San Mateo, CA: Morgan Kaufmann, 1990.

11
J. H. Schmidhuber.
Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments.
Technical Report FKI-126-90 (revised), Institut für Informatik, Technische Universität München, November 1990.
(Revised and extended version of an earlier report from February.).

12
J. H. Schmidhuber.
An on-line algorithm for dynamic reinforcement learning and planning in reactive environments.
In Proc. IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 2, pages 253-258, 1990.

13
J. H. Schmidhuber.
Towards compositional learning with dynamic neural networks.
Technical Report FKI-129-90, Institut für Informatik, Technische Universität München, 1990.

14
J. H. Schmidhuber.
A possibility for implementing curiosity and boredom in model-building neural controllers.
In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222-227. MIT Press/Bradford Books, 1991.

15
J. H. Schmidhuber.
Reinforcement learning in markovian and non-markovian environments.
In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, pages 500-506. San Mateo, CA: Morgan Kaufmann, 1991.

16
C. Watkins.
Learning from Delayed Rewards.
PhD thesis, King's College, 1989.

17
P. J. Werbos.
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences.
PhD thesis, Harvard University, 1974.

18
P. J. Werbos.
Generalization of backpropagation with application to a recurrent gas market model.
Neural Networks, 1, 1988.

19
P. J. Werbos.
Backpropagation and neurocontrol: A review and prospectus.
In IEEE/INNS International Joint Conference on Neural Networks, Washington, D.C., volume 1, pages 209-216, 1989.

20
S.D. Whitehead and D. H. Ballard.
Active perception and reinforcement learning.
Technical Report 331, University of Rochester, Dept. of Comp. Sci., 1990.

21
R. J. Williams.
On the use of backpropagation in associative reinforcement learning.
In IEEE International Conference on Neural Networks, San Diego, volume 2, pages 263-270, 1988.



Juergen Schmidhuber 2003-02-21

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