Reinforcement Learning - Design of Experiments for Reinforcement Learning - page 43

Coulom, R. (2002b). Reinforcement learning using neural networks, with applications to motor
control . Unpublished PhD dissertation, National Polytechnic Institute of Grenoble, Grenoble,
France.
Dann, C., Neumann, G., & Peters, J. (2014). Policy evaluation with temporal differences: A survey
and comparison. Journal of Machine Learning Research , 15(1), 809-883.
Dayan, P. (1993). Improving generalization for temporal difference learning: The successor
representation. Neural Computation , 5(4), 613-624.
Dayan, P. & Niv, Y. (2008). Reinforcement learning: The good, the bad and the ugly. Current
Opinion in Neuroscience , 18(2), 185-196.
Dietterich, T. G. (2000). Ensemble methods in machine learning. In Proceedings of the 1st Interna-
tional Workshop on Multiple Classifier Systems (MCS), Cagliari, Italy, 21-23 June (pp. 1-15).
doi: 10.1007/3-540-45014-9_1
Doya, K. (1996). Temporal difference learning in continuous time and space. In Touretzky, D. S.,
Mozer, M. C., & Hasselmo, M. E. (Eds.), Advances in Neural Information Processing Systems
8 (pp. 1073-1079). Cambridge, MA: MIT Press.
Doya, K. (2000). Reinforcement learning in continuous time and space. Neural Computation , 12(1),
219-245.
Elman, J. L. (1990). Finding structure in time. Cognitive Science , 14(2), 179-211.
Fairbanks, M. & Alonso, E. (2012). The divergence of reinforcement learning algorithms with value-
iteration and function approximation. In Proceedings of the 2012 International Joint Conference
on Neural Networks (IJCNN), Brisbane, Queensland, Australia, 10-15 June (pp. 1-8). doi:
10.1109/IJCNN.2012.6252792
Främling, K. (2008). Light-weight reinforcement learning with function approximation for real-
life control tasks. In Filipe, J., Andrade-Cetto, J., & Ferrier, J.-L. (Eds.), Proceedings of the
5th International Conference on Informatics in Control, Automation and Robotics, Intelligent
Control Systems and Optimization (ICINCO-ICSO), Funchal, Madeira, Portugal, 11-15 May
(pp. 127-134). INSTICC Press.
Gabel, T. & Riedmiller, M. (2007). On a successful application of multi-agent reinforcement
learning to operations research benchmarks. In Proceedings of the 2007 IEEE Symposium on
Approximate Dynamic Programming and Reinforcement Learning (ADPRL 2007), Honolulu,
HI, 1-5 April (pp. 69-75). doi: 10.1109/ADPRL.2007.368171
Gabel, T., Lutz, C., & Riedmiller, M. (2011). Improved neural fitted Q iteration applied to a
novel computer gaming and learning benchmark. In Proceedings of the 2011 IEEE Symposium
on Approximate Dynamic Programming and Reinforcement Learning (ADPRL 2011), Paris,
France, 11-15 April (pp. 279-286). doi: 10.1109/ADPRL.2011.5967361
Galichet, N., Sebag, M., & Teytaud, O. (2013). Exploration vs. exploitation vs safety: Risk-aware
multi-armed bandits. In Proceedings of the Asian Conference on Machine Learning (ACML
2013), Canberra, ACT, Australia, 13-15 November (pp. 245-260). Journal of Machine Learning
Research (JMLR): Workshop and Conference Proceedings.
Gatti, C. J. & Embrechts, M. J. (2012). Reinforcement learning with neural networks: Tricks of
the trade. In Georgieva, P., Mihayolva, L., & Jain, L. (Eds.), Advances in Intelligent Signal
Processing and Data Mining (pp. 275-310). New York, NY: Springer-Verlag.
Gatti, C. J., Embrechts, M. J., & Linton, J. D. (2011a). Parameter settings of reinforcement learning
for the game of Chung Toi. In Proceedings of the 2011 IEEE International Conference on
Systems, Man, and Cybernetics (SMC 2011), Anchorage, AK, 9-12 October (pp. 3530-3535).
doi: 10.1109/ICSMC.2011.6084216
Gatti, C. J., Linton, J. D., & Embrechts, M. J. (2011b). A brief tutorial on reinforcement learning:
The game of Chung Toi. In Proceedings of the 19th European Symposium on Articial Neural
Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 27-
29 April (pp. 129-134). Bruges, Belgium: ESANN.