Characterizing reinforcement learning methods through parameterized learning problems
Shivaram Kalyanakrishnan and Peter Stone, 2011
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On Learning with Imperfect Representations
Shivaram Kalyanakrishnan and Peter Stone, 2011
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Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning
Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter Stone, 2011
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Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
Carlton Downey and Scott Sanner, 2010
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Toward Off-Policy Learning Control with Function Approximation
Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, and Richard S. Sutton, 2010
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Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Marek Petrik, Gavin Taylor, Ron Parr, and Shlomo Zilberstein, 2010
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The adaptive $k$-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
Carlos Diuk, Lihong Li, and Bethany R. Leffler, 2009
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Improving Optimistic Exploration in Model-Free Reinforcement Learning
Marek Grze\'s and Daniel Kudenko, 2009
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A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion
Nikolaus Hansen, André S.P. Niederberger, Lino Guzzella, and Petros Koumoutsakos, 2009
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Regularization and feature selection in least-squares temporal difference learning
J. Zico Kolter and Andrew Y. Ng, 2009
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Learning Representation and Control in Markov Decision Processes: New Frontiers
Sridhar Mahadevan, 2009
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Ontogenetic and Phylogenetic Reinforcement Learning
Julian Togelius, Tom Schaul, Daan Wierstra, Christian Igel, Faustino Gomez, and Jürgen Schmidhuber, 2009
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Generalized Domains for Empirical Evaluations in Reinforcement Learning
Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter Stone, 2009
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A Theoretical and Empirical Analysis of Expected Sarsa
Harm van Seijen, Hado van Hasselt, Shimon Whiteson, and Marco Wiering, 2009
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An empirical evaluation of supervised learning in high dimensions
Rich Caruana, Nikolaos Karampatziakis, and Ainur Yessenalina, 2008
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Temporal Difference Updating without a Learning Rate
Marcus Hutter and Shane Legg, 2008
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The many faces of optimism: a unifying approach
Istvan Szita and András Lörincz, 2008
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SATzilla: Portfolio-based Algorithm Selection for SAT
Lin Xu, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown, 2008
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Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark
Martin Riedmiller, Jan Peters, and Stefan Schaal, 2007
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An empirical comparison of supervised learning algorithms
Rich Caruana and Alexandru Niculescu-Mizil, 2006
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Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Thomas Degris, Olivier Sigaud, and Pierre-Henri Wuillemin, 2006
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Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming
Abraham P. George and Warren B. Powell, 2006
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Function Approximation via Tile Coding: Automating Parameter Choice
Alexander A. Sherstov and Peter Stone, 2005
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A Robot that Reinforcement-Learns to Identify and Memorize Important Previous Observations
Bram Bakker, Viktor Zhumatiy, Gabriel Gruener, and Jürgen Schmidhuber, 2003
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Boosting as a Metaphor for Algorithm Design
Kevin Leyton-Brown, Eugene Nudelman, Galen Andrew, Jim McFadden, and Yoav Shoham, 2003
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Using MDP Characteristics to Guide Exploration in Reinforcement Learning
Bohdana Ratitch and Doina Precup, 2003
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Characterizing Markov Decision Processes
Bohdana Ratitch and Doina Precup, 2002
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A Perspective View and Survey of Meta-Learning
Ricardo Vilalta and Youssef Drissi, 2002
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Scaling to Very Very Large Corpora for Natural Language Disambiguation
Michele Banko and Eric Brill, 2001
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Random Forests
Leo Breiman, 2001
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Convergence of Optimistic and Incremental Q-Learning
Eyal Even-Dar and Yishay Mansour, 2001
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Algorithm portfolios
Carla P. Gomes and Bart Selman, 2001
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Local Search Algorithms for SAT: An Empirical Evaluation
Holger H. Hoos and Thomas Stützle, 2000
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Meta-Learning by Landmarking Various Learning Algorithms
Bernhard Pfahringer, Hilan Bensusan, and Christophe Giraud-Carrier, 2000
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An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Eric Bauer and Ron Kohavi, 1999
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Symposium on Applications of Reinforcement Learning: Final Report for NSF Grant IIS-9810208
Pat Langley and Mark Pendrith, 1998
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Experiments with a New Boosting Algorithm
Yoav Freund and Robert E. Schapire, 1996
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Incremental Multi-Step Q-Learning
Jing Peng and Ronald J. Williams, 1996
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Bagging, Boosting, and C4.5
J. Ross Quinlan, 1996
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Recursive Automatic Bias Selection for Classifier Construction
Carla E. Brodley, 1995
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Problem Solving with Reinforcement Learning
Gavin Adrian Rummery, 1995
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Using a Genetic Algorithm to Search for the Representational Bias of a Collective Reinforcement Learner
Helen G. Cobb and Peter Bock, 1994
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An Optimization-based Categorization of Reinforcement Learning Environments
Michael L. Littman, 1993
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Interactions between Learning and Evolution
David Ackley and Michael Littman, 1992
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Inductive Biases in a Reinforcement Learner
Helen G. Cobb, 1992
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Learning from Delayed Rewards
Christopher John Cornish Hellaby Watkins, 1989
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How Evaluation Guides AI Research: The Message Still Counts More than the Medium
Paul R. Cohen and Adele E. Howe, 1988
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Machine Learning as an Experimental Science
Pat Langley, 1988
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Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm
Nick Littlestone, 1987
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Brains, Behavior and Robotics
James Sacra Albus, 1981
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