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Gatti, C. J., Embrechts, M. J., & Linton, J. D. (2013). An empirical analysis of reinforcement
learning using design of experiments. In Proceedings of the 21st European Symposium on Arti-
ficial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges,
Belgium, 24-26 April (pp. 221-226). Bruges, Belgium: ESANN.
Gers, F. (2001). Long short-term memory in recurrent neural networks . Unpublished PhD
dissertation, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Ghory,
I.
(2004).
Reinforcement
learning
in
board
games
(Technical
Report
CSTR-
04-004,
Department
of
Computer
Science,
University
of
Bristol).
Retrieved
from
http://www.cs.bris.ac.uk/Publications/Papers/2000100.pdf.
Gordon, G. J. (1995). Stable function approximation in dynamic programming. In Proceedings of
the 12th International Conference on Machine Learning (ICML), Tahoe City, CA, 9-12 July
(pp. 261-268). San Francisco, CA: Morgan Kaufmann.
Gordon, G. J. (2001). Reinforcement learning with function approximation converges to a region.
In Advances in Neural Information Processing Systems 13 (pp. 1040-1046). Cambridge, MA:
MIT Press.
Gorse, D. (2011). Application of stochastic recurrent reinforcement learning to index trading. In
European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine
Learning (ESANN), Bruges, Belgium, 27-29 April (pp. 123-128). Bruges, Belgium: ESANN.
Gosavi, A., Bandla, N., & Das, T. K. (2002). A reinforcement learning approach to a single leg airline
revenue management problem with multiple fare classes and overbooking. IIE Transactions ,
34(9), 729-742.
Grüning, A. (2007). Elman backpropagation as reinforcement for simple recurrent networks. Neural
Computation , 19(11), 3108-3131.
Günther, M. (2008). Automatic feature construction for general game playing . Unpublished masters
thesis, Dresden University of Technology, Dresden, Germany.
Hafner, R. & Riedmiller, M. (2011). Reinforcement learning in feedback control. Machine
Learning , 84(1-2), 137-169.
Hans, A. & Udluft, S. (2010). Ensembles of neural networks for robust reinforcement learning.
In Proceedings of the 9th International Conference on Machine Learning and Applications
(ICMLA), Washington D.C., 12-14 December (pp. 401-406). doi: 10.1109/ICMLA.2010.66
Hans, A. & Udluft, S. (2011). Ensemble usage for more reliable policy identification in rein-
forcement learning. In European Symposium on Artificial Neural Networks, Computational
Intelligence, and Machine Learning (ESANN), Bruges, Belgium, 27-29 April (pp. 165-170).
Bruges, Belgium: ESANN.
Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation , 9(8),
1735-1780.
Hoffmann, A. & Freier, B. (1996). On integrating domain knowledge into reinforcement learning.
In International Conference on Neural Information Processing (ICONIP), Hong Kong, China,
24-27 September (pp. 954-959). Singapore: Springer-Verlag.
Igel, C. (2003). Neuroevolution for reinforcement learning using evolution strategies. In Proceed-
ings from the 2003 Conference on Evolutionary Computing (CEC), Canberra, Australia, 8-12
December (Vol. 4, pp. 2588-2595). doi: 10.1109/CEC.2003.1299414
Jaakkola, T., Singh, S. P., & Jordan, M. I. (1995). Reinforcement learning algorithm for partially
observable Markov decision problem. In Advances in Neural Information Processing Systems
7 (pp. 345-352). Cambridge, MA: MIT Press.
Jaakkola, T., Jordan, M. I., & Singh, S. P. (2003). On the convergence of stochastic iterative dynamic
programming algorithms. Neural Computation , 6(6), 1185-1201.
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal
of Artificial Intelligence Research , 4, 237-285.
Kalyanakrishnan, S. & Stone, P. (2007). Batch reinforcement learning in a complex domain. In
Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent
Systems (AAMAS07), Honolulu, HI, 14-18 May (pp. 650-657). doi: 10.1145/1329125.1329241
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