Leemon Baird. Residual algorithms: Reinforcement learning with function approximation. In Proceedings of the 12th International Conference on Machine Learning (ICML 1995), pages 30-37. Morgan Kaufmann, 1995. [OpenAIRE]
 Marc Bellemare, Joel Veness, and Michael Bowling. Sketch-based linear value function approximation. In Advances in Neural Information Processing Systems 25, pages 2222-2230, 2012.
 Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47:253-279, 2013.
 Marc G Bellemare, Joel Veness, and Michael Bowling. Investigating contingency awareness using atari 2600 games. In AAAI, 2012.
 Marc G. Bellemare, Joel Veness, and Michael Bowling. Bayesian learning of recursively factored environments. In Proceedings of the Thirtieth International Conference on Machine Learning (ICML 2013), pages 1211-1219, 2013.
 George E. Dahl, Dong Yu, Li Deng, and Alex Acero. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 20(1):30 -42, January 2012.
 Alex Graves, Abdel-rahman Mohamed, and Geoffrey E. Hinton. Speech recognition with deep recurrent neural networks. In Proc. ICASSP, 2013.
 Matthew Hausknecht, Risto Miikkulainen, and Peter Stone. A neuro-evolution approach to general atari game playing. 2013.
 Nicolas Heess, David Silver, and Yee Whye Teh. Actor-critic reinforcement learning with energy-based policies. In European Workshop on Reinforcement Learning, page 43, 2012.
 Kevin Jarrett, Koray Kavukcuoglu, MarcAurelio Ranzato, and Yann LeCun. What is the best multi-stage architecture for object recognition? In Proc. International Conference on Computer Vision and Pattern Recognition (CVPR 2009), pages 2146-2153. IEEE, 2009. [OpenAIRE]
 Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, pages 1106-1114, 2012.
 Sascha Lange and Martin Riedmiller. Deep auto-encoder neural networks in reinforcement learning. In Neural Networks (IJCNN), The 2010 International Joint Conference on, pages 1-8. IEEE, 2010.
 Long-Ji Lin. Reinforcement learning for robots using neural networks. Technical report, DTIC Document, 1993.
 Hamid Maei, Csaba Szepesvari, Shalabh Bhatnagar, Doina Precup, David Silver, and Rich Sutton. Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. In Advances in Neural Information Processing Systems 22, pages 1204-1212, 2009.
 Hamid Maei, Csaba Szepesva´ri, Shalabh Bhatnagar, and Richard S. Sutton. Toward off-policy learning control with function approximation. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pages 719-726, 2010.