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All publications by Ronald Parr
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Greedy Algorithms for Sparse Reinforcement Learning
Christopher Painter-wakefield and Ronald Parr
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Marek Petrik, Gavin Taylor, Ronald Parr and Shlomo Zilberstein
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Linear Complementarity for Regularized Policy Evaluation and Improvement
Jeffrey Johns, Christopher Painter-wakefield and Ronald Parr
Advances in Neural Information Processing Systems 23, 2010


Kernelized value function approximation for reinforcement learning
Gavin Taylor and Ronald Parr
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-wakefield and Michael L. Littman
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Analyzing feature generation for value-function approximation
Ronald Parr, Christopher Painter-wakefield, Lihong Li and Michael L. Littman
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Learning probabilistic motion models for mobile robots
Austin I. Eliazar and Ronald Parr
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Reinforcement Learning as Classification: Leveraging Modern Classifiers
Michail G. Lagoudakis and Ronald Parr
Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003


Least-Squares Policy Iteration
Michail G. Lagoudakis and Ronald Parr
Journal of Machine Learning Research, 2003


Learning in Zero-Sum Team Markov Games Using Factored Value Functions
Michail G. Lagoudakis and Ronald Parr
Advances in Neural Information Processing Systems 15, 2002