Agnostic KWIK learning and efficient approximate reinforcement learning
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[1] Kai Lai Chung,et al. A Course in Probability Theory , 1949 .
[2] J. Doob. Stochastic processes , 1953 .
[3] D. Freedman. On Tail Probabilities for Martingales , 1975 .
[4] Martin L. Puterman,et al. Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .
[5] Claude-Nicolas Fiechter,et al. Efficient reinforcement learning , 1994, COLT '94.
[6] Ronen I. Brafman,et al. A near-optimal polynomial time algorithm for learning in certain classes of stochastic games , 2000, Artif. Intell..
[7] Ronen I. Brafman,et al. R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning , 2001, J. Mach. Learn. Res..
[8] Sham M. Kakade,et al. On the sample complexity of reinforcement learning. , 2003 .
[9] Michael Kearns,et al. Near-Optimal Reinforcement Learning in Polynomial Time , 1998, Machine Learning.
[10] Michael L. Littman,et al. A theoretical analysis of Model-Based Interval Estimation , 2005, ICML.
[11] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[12] Lihong Li,et al. Incremental Model-based Learners With Formal Learning-Time Guarantees , 2006, UAI.
[13] Michael L. Littman,et al. Efficient Structure Learning in Factored-State MDPs , 2007, AAAI.
[14] Alexander L. Strehl,et al. Model-Based Reinforcement Learning in Factored-State MDPs , 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.
[15] Michael L. Littman,et al. Online Linear Regression and Its Application to Model-Based Reinforcement Learning , 2007, NIPS.
[16] Thomas J. Walsh,et al. Knows what it knows: a framework for self-aware learning , 2008, ICML '08.
[17] Peter Auer,et al. Near-optimal Regret Bounds for Reinforcement Learning , 2008, J. Mach. Learn. Res..
[18] András Lörincz,et al. The many faces of optimism: a unifying approach , 2008, ICML '08.
[19] Andre Cohen,et al. An object-oriented representation for efficient reinforcement learning , 2008, ICML '08.
[20] Lihong Li,et al. Reinforcement Learning in Finite MDPs: PAC Analysis , 2009, J. Mach. Learn. Res..
[21] Michael L. Littman,et al. A unifying framework for computational reinforcement learning theory , 2009 .
[22] Csaba Szepesvári,et al. Model-based reinforcement learning with nearly tight exploration complexity bounds , 2010, ICML.
[23] Lihong Li,et al. Reducing reinforcement learning to KWIK online regression , 2010, Annals of Mathematics and Artificial Intelligence.
[24] Thomas J. Walsh,et al. Integrating Sample-Based Planning and Model-Based Reinforcement Learning , 2010, AAAI.
[25] Csaba Szepesvári,et al. Algorithms for Reinforcement Learning , 2010, Synthesis Lectures on Artificial Intelligence and Machine Learning.