Shivaram's Reading List


Function Approximation     Partial Observability     Learning Methods     Ensembles    
Stochastic Optimisation     General RL     General ML     Multiagent Learning    
Comparison/Integration     Bandits     Applications     Robot Soccer    
Humanoids     Parameter     MDP     Empirical    
Failure Warning     Representation     General AI     Neural Networks    
All    

Parameter

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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