Learning Methods for Sequential Decision Making with Imperfect Representations
Shivaram Kalyanakrishnan, 2011
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Near-optimal Regret Bounds for Reinforcement Learning
Thomas Jaksch, Ronald Ortner, and Peter Auer, 2010
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Algorithms for Reinforcement Learning
Csaba Szepesvári, 2010
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Evolving Neural Networks for Strategic Decision-Making Problems
Nate Kohl and Risto Miikkulainen, 2009
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Reinforcement learning in the brain
Yael Niv, 2009
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Reinforcement Learning in Finite MDPs: PAC Analysis
Lihong Strehl, Alexander L., Li and Michael L. Littman, 2009
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On the role of tracking in stationary environments
Richard S. Sutton, Anna Koop, and David Silver, 2007
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Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
Matthew E. Taylor, Peter Stone, and Yaxin Liu, 2007
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PAC model-free reinforcement learning
Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, and Michael L. Littman, 2006
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Why (PO)MDPs Lose for Spatial Tasks and What to Do About It
Terran Lane and William D. Smart, 2005
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A theoretical analysis of Model-Based Interval Estimation
Alexander L. Strehl and Michael L. Littman, 2005
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Temporal difference models describe higher-order learning in humans
Ben Seymour, John P. O'Doherty, Peter Dayan, Martin Koltzenburg, Anthony K. Jones, Raymond J. Dolan, Karl J. Friston, and Richard S. Frackowiak, 2004
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R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I. Brafman and Moshe Tennenholtz, 2003
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Near-Optimal Reinforcement Learning in Polynomial Time
Michael Kearns and Satinder Singh, 2002
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Eligibility Traces for Off-Policy Policy Evaluation
Doina Precup, Richard S. Sutton, and Satinder P. Singh, 2000
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Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms
Satinder Singh, Tommi Jaakkola, Michael L. Littman, and Csaba Szepesvári, 2000
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Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
Andrew Y. Ng, Daishi Harada, and Stuart J. Russell, 1999
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Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
Jette Randløv and Preben Alstrøm, 1998
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto, 1998
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No free lunch theorems for optimization
David H. Wolpert and William G. Macready, 1997
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Neuro-Dynamic Programming
Dimitri P. Bertsekas and John N. Tsitsiklis, 1996
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On the Complexity of Solving Markov Decision Problems
Michael L. Littman, Thomas L. Dean, and Leslie Pack Kaelbling, 1995
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Markov Decision Processes
Martin L. Puterman, 1994
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On-line Q-learning using connectionist systems
G. A. Rummery and M. Niranjan, 1994
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On bias and step size in temporal-difference learning
Richard S. Sutton and Satinder P. Singh, 1994
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Practical Issues in Temporal Difference Learning
Gerald Tesauro, 1992
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Q-Learning
Christopher J. C. H. Watkins and Peter Dayan, 1992
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Learning to Predict By the Methods of Temporal Differences
Richard S. Sutton, 1988
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Dynamic Programming
Richard Bellman, 1957
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Some aspects of the sequential design of experiments
Herbert Robbins, 1952
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