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    

Function Approximation

A Brief Survey of Parametric Value Function Approximation
Matthieu Geist and Olivier Pietquin, 2010
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Finite-Sample Analysis of LSTD
Alessandro Lazaric, Mohammad Ghavamzadeh, and Rémi Munos, 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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Feature Selection for Value Function Approximation Using Bayesian Model Selection
Tobias Jung and Peter Stone, 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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Feature Discovery in Approximate Dynamic Programming
Philippe Preux, Sertan Girgin, and Manuel Loth, 2009
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Fast gradient-descent methods for temporal-difference learning with linear function approximation
Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, and Eric Wiewiora, 2009
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Feature Discovery in Reinforcement Learning Using Genetic Programming
Sertan Girgin and Philippe Preux, 2008
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Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and Applications
William B. Langdon, Riccardo Poli, Nicholas Freitag McPhee, and John R. Koza, 2008
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A worst-case comparison between temporal difference and residual gradient with linear function approximation
Lihong Li, 2008
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An analysis of reinforcement learning with function approximation
Francisco S. Melo, Sean P. Meyn, and M. Isabel Ribeiro, 2008
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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, 2008
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Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping
Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, and Michael Bowling, 2008
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Learning RoboCup-Keepaway with Kernels
Tobias Jung and Daniel Polani, 2007
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Learning classifier systems: a survey
Olivier Sigaud and Stewart W. Wilson, 2007
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Adaptive Representations for Reinforcement Learning
Shimon Azariah Whiteson, 2007
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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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Tree-Based Batch Mode Reinforcement Learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel, 2005
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Basis Function Adaptation in Temporal Difference Reinforcement Learning
Ishai Menache, Shie Mannor, and Nahum Shimkin, 2005
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Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method
Martin Riedmiller, 2005
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Sparse cooperative Q-learning
Jelle R. Kok and Nikos Vlassis, 2004
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Convergence of synchronous reinforcement learning with linear function approximation
Artur Merke and Ralf Schoknecht, 2004
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Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning
Bohdana Ratitch and Doina Precup, 2004
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Least-Squares Policy Iteration
Michail G. Lagoudakis and Ronald Parr, 2003
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Reinforcement Learning as Classification: Leveraging Modern Classifiers
Michail G. Lagoudakis and Ronald Parr, 2003
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Least Squares Policy Evaluation Algorithms with Linear Function Approximation
A. Nedić and D. P. Bertsekas, 2003
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A Convergent Form of Approximate Policy Iteration
Theodore J. Perkins and Doina Precup, 2003
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Optimality of Reinforcement Learning Algorithms with Linear Function Approximation
Ralf Schoknecht, 2003
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Technical Update: Least-Squares Temporal Difference Learning
Justin A. Boyan, 2002
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Variable Resolution Discretization in Optimal Control
Rémi Munos and Andrew Moore, 2002
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Kernel-Based Reinforcement Learning
Dirk Ormoneit and Śaunak Sen, 2002
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Batch Value Function Approximation via Support Vectors
Thomas G. Dietterich and Xin Wang, 2001
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Max-norm Projections for Factored MDPs
Carlos Guestrin, Daphne Koller, and Ronald Parr, 2001
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Off-Policy Temporal Difference Learning with Function Approximation
Doina Precup, Richard S. Sutton, and Sanjoy Dasgupta, 2001
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On the Convergence of Temporal-Difference Learning with Linear Function Approximation
Vladislav Tadić, 2001
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Policy Iteration for Factored MDPs
Daphne Koller and Ronald Parr, 2000
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Policy Gradient Methods for Reinforcement Learning with Function Approximation
Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour, 2000
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Convergence of Reinforcement Learning With General Function Approximators
Vassilis A. Papavassiliou and Stuart Russell, 1999
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto, 1998
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Learning and Value Function Approximation in Complex Decision Processes
Benjamin Van Roy, 1998
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An analysis of temporal-difference learning with function approximation
John N. Tsitsiklis and Benjamin Van Roy, 1997
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Linear Least-Squares Algorithms for Temporal Difference Learning
Steven J. Bradtke and Andrew G. Barto, 1996
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Stable Fitted Reinforcement Learning
Geoffrey J. Gordon, 1996
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Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding
Richard S. Sutton, 1996
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Feature-based methods for large scale dynamic programming
John N. Tsitsiklis and Benjamin Van Roy, 1996
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Residual Algorithms: Reinforcement Learning with Function Approximation
Leemon Baird, 1995
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A Counterexample to Temporal Differences Learning
Dimitri P. Bertsekas, 1995
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Generalization in Reinforcement Learning: Safely Approximating the Value Function
Justin A. Boyan and Andrew W. Moore, 1995
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Stable Function Approximation in Dynamic Programming
Geoffrey J. Gordon, 1995
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The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces
Andrew W. Moore and Christopher G. Atkeson, 1995
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Reinforcement Learning with Soft State Aggregation
Satinder P. Singh, Tommi Jaakkola, and Michael I. Jordan, 1995
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TD($łambda$) Converges with Probability 1
Peter Dayan and Terrence J. Sejnowski, 1994
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An Upper Bound on the Loss from Approximate Optimal-Value Functions
Satinder P. Singh and Richard C. Yee, 1994
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Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions
Ronald J. Williams and Leemon C. Baird III, 1994
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Reinforcement Learning Applied to Linear Quadratic Regulation
Steven J. Bradtke, 1993
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Approximating Q-Values with Basis Function Representations
Philip Sabes, 1993
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Online Learning with Random Representations
Richard S. Sutton and Steven D. Whitehead, 1993
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Issues in Using Function Approximation for Reinforcement Learning
Sebastian Thrun and Anton Schwartz, 1993
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The Convergence of TD($łambda$) for General $łambda$
Peter Dayan, 1992
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Practical Issues in Temporal Difference Learning
Gerald Tesauro, 1992
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Learning to Predict By the Methods of Temporal Differences
Richard S. Sutton, 1988
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