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	Automatic basis function construction for approximate dynamic programming and reinforcement learning [My Copy] In ICML '06: Proceedings of the 23rd international conference on Machine learning (2006), pp. 449-456. by Philipp W. Keller, Shie Mannor, Doina Precup posted to rl by stober on 2009-12-15 21:06:52 as along with 1 person tdahl
	Predictive Projections [My Copy] In roceedings of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09) (July 2009) by Nathan Sprague posted to rl by stober on 2009-12-14 23:26:59 as
	HQ-learning [My Copy] Adapt. Behav., Vol. 6, No. 2. (1997), pp. 219-246. by Marco Wiering, Jürgen Schmidhuber posted to rl by stober on 2009-11-20 23:07:03 as
	Policy gradient methods for reinforcement learning with function approximation [My Copy] Advances in neural information processing systems, Vol. 12 (2000), pp. 1057-1063. by R. S. Sutton, D. McAllester, S. Singh, Y. Mansour posted to rl by stober on 2009-11-20 22:57:19 as
	Efficient structure learning in factored-state MDPs [My Copy] In PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, Vol. 22, No. 1. (2007) by A. L. Strehl, C. Diuk, M. L. Littman posted to rl by stober on 2009-10-20 20:31:37 as
	Learning classifier systems: a survey [My Copy] Soft Computing-A Fusion of Foundations, Methodologies and Applications, Vol. 11, No. 11. (2007), pp. 1065-1078. by O. Sigaud, S. W. Wilson posted to rl lcs by stober on 2009-10-20 20:12:15 as
	The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning [My Copy] In Proceedings of the 26th Annual International Conference on Machine Learning (2009) by C. Diuk, L. Li, B. R. Leffler posted to rl by stober on 2009-10-20 20:11:14 as
	An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning [My Copy] In ICML '08: Proceedings of the 25th international conference on Machine learning (2008), pp. 752-759. by Ronald Parr, Lihong Li, Gavin Taylor, Christopher P. Wakefield, Michael L. Littman posted to rl by stober on 2009-09-24 20:48:02 as
	The complexity of Markov decision processes [My Copy] Math. Oper. Res., Vol. 12, No. 3. (August 1987), pp. 441-450. by Christos Papadimitriou, John N. Tsitsiklis posted to rl by stober on 2009-09-24 01:08:28 as along with 2 people and 4 groups euclid AlainDutech Algorithms metric_embeddings TCS Polytopes
	On the Complexity of Solving Markov Decision Problems [My Copy] In In Proc. of the Eleventh International Conference on Uncertainty in Artificial Intelligence (1995), pp. 394-402. by Michael L. Littman, Thomas L. Dean, Leslie P. Kaelbling posted to rl by stober on 2009-09-24 01:07:38 as
	PAC model-free reinforcement learning [My Copy] In ICML '06: Proceedings of the 23rd international conference on Machine learning (2006), pp. 881-888. by Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman posted to rl by stober on 2009-09-24 01:01:14 as
	Apprenticeship learning via inverse reinforcement learning [My Copy] In Proceedings of the twenty-first international conference on Machine learning (2004) by P. Abbeel, A. Y. Ng posted to rl by stober on 2009-09-24 00:33:26 as
	Reinforcement Learning in Continuous Action Spaces [My Copy] Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on In Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on (2007), pp. 272-279. by H. van Hasselt, M. A. Wiering posted to rl by stober on 2009-07-08 20:25:18 as along with 1 person Cavadini
	Algorithms for Inverse Reinforcement Learning [My Copy] In in Proc. 17th International Conf. on Machine Learning (2000), pp. 663-670. by Andrew Y. Ng, Stuart Russell posted to rl by stober on 2009-07-02 17:01:10 as along with 2 people zhensong Maderlock
	Models of Information Processing in the Sensorimotor Loop [My Copy] Information Theory and Statistical Learning (2009), pp. 289-308. by Daniel Polani, Marco Möller posted to rl by stober on 2009-06-16 21:05:10 as
	All Else Being Equal Be Empowered [My Copy] Advances in Artificial Life (2005), pp. 744-753. by Alexander S. Klyubin, Daniel Polani, Chrystopher L. Nehaniv posted to rl by stober on 2009-06-16 21:03:15 as
	Tree-Based Batch Mode Reinforcement Learning [My Copy] J. Mach. Learn. Res., Vol. 6 (2005), pp. 503-556. by Damien Ernst, Pierre Geurts, Louis Wehenkel posted to rl by stober on 2009-04-08 18:50:39 as
	Near-Optimal Reinforcement Learning in Polynomial Time [My Copy] Mach. Learn., Vol. 49, No. 2-3. (2002), pp. 209-232. by Michael Kearns, Satinder Singh posted to rl by stober on 2009-04-08 18:40:32 as
	Using expectation-maximization for reinforcement learning [My Copy] Neural Comput., Vol. 9, No. 2. (1997), pp. 271-278. by Peter Dayan, Geoffrey E. Hinton posted to em rl by stober on 2009-04-06 03:59:57 as
	Learning the structure of Factored Markov Decision Processes in reinforcement learning problems [My Copy] In ICML '06: Proceedings of the 23rd international conference on Machine learning (2006), pp. 257-264. by Thomas Degris, Olivier Sigaud, Pierre H. Wuillemin posted to rl by stober on 2009-04-06 03:18:07 as
	Toward a topological theory of relational reinforcement learning for navigation tasks [My Copy] In Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS-2005) (2005) by T. Lane, A. Wilson posted to rl by stober on 2009-04-06 03:15:30 as
	Efficient reinforcement learning with relocatable action models [My Copy] In PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, Vol. 22, No. 1. (2007) by B. R. Leffler, M. L. Littman, T. Edmunds posted to rl by stober on 2009-04-06 03:13:42 as
	Automated state abstraction for options using the u-tree algorithm [My Copy] In In Advances in Neural Information Processing Systems: Proceedings of the 2000 Conference, Vol. 13 (2001), pp. 1054-1060. by Anders Jonsson, Andrew G. Barto posted to rl by stober on 2009-04-06 03:05:20 as
	ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning [My Copy] In ICML '08: Proceedings of the 25th international conference on Machine learning (2008), pp. 600-607. by Nicolas Loeff, David Forsyth, Deepak Ramachandran posted to manifold ml rl by stober on 2009-03-31 03:59:47 as
	An analysis of temporal-difference learning with function approximation [My Copy] Automatic Control, IEEE Transactions on In Automatic Control, IEEE Transactions on, Vol. 42, No. 5. (1997), pp. 674-690. by J. N. Tsitsiklis, B. Van Roy posted to rl by stober on 2009-03-24 22:54:08 as
	From Pixels to Policies: A Bootstrapping Agent [My Copy] In 7th IEEE International Conference on Development and Learning (2008), pp. 103-108. by J. Stober, B. Kuipers posted to icdl2008 rl by stober on 2009-02-03 01:24:28 as
	Speeding up Reinforcement Learning using Manifold Representations: Preliminary Results [My Copy] In Proceedings of the {IJCAI} 2005 Workshop on Reasoning with Uncertainty in Robotics ({RUR 05}) (July 2005) by Robert Glaubius, Motoi Namihira, William D. Smart posted to rl by stober on 2008-12-03 21:13:42 as
	Manifold Representations for Value-Function Approximation in Reinforcement Learning [My Copy] No. {WUCSE-2005-19}. (2005) by Robert Glaubius, William Smart posted to rl by stober on 2008-12-03 21:13:14 as
	Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning [My Copy] Artificial Intelligence, Vol. 112, No. 1-2. (1999), pp. 181-211. by Richard S. Sutton, Doina Precup, Satinder Singh posted to options rg rl by stober on 2008-08-13 17:06:44 as along with 5 people pcarbo Maderlock robotact schumach ddahlem

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