论文信息 - A theoretical analysis of Model-Based Interval Estimation

A theoretical analysis of Model-Based Interval Estimation

Several algorithms for learning near-optimal policies in Markov Decision Processes have been analyzed and proven efficient. Empirical results have suggested that Model-based Interval Estimation (MBIE) learns efficiently in practice, effectively balancing exploration and exploitation. This paper presents the first theoretical analysis of MBIE, proving its efficiency even under worst-case conditions. The paper also introduces a new performance metric, average loss, and relates it to its less "online" cousins from the literature.

Michael L. Littman | Alexander L. Strehl | M. Littman

[1] Jürgen Schmidhuber,et al. Efficient model-based exploration , 1998 .

[2] Ronen I. Brafman,et al. R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning , 2001, J. Mach. Learn. Res..

[3] E. Ordentlich,et al. Inequalities for the L1 Deviation of the Empirical Distribution , 2003 .

[4] Martin L. Puterman,et al. Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[5] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[6] Leslie Pack Kaelbling,et al. Learning in embedded systems , 1993 .

[7] Philip W. L. Fong. A Quantitative Study of Hypothesis Selection , 1995, ICML.

[8] Sham M. Kakade,et al. On the sample complexity of reinforcement learning. , 2003 .

[9] Stewart W. Wilson,et al. From Animals to Animats 5. Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior , 1997 .

[10] Claude-Nicolas Fiechter. Expected Mistake Bound Model for On-Line Reinforcement Learning , 1997, ICML.

[11] Michael Kearns,et al. Near-Optimal Reinforcement Learning in Polynomial Time , 1998, Machine Learning.

[12] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .

[13] Michael L. Littman,et al. An empirical evaluation of interval estimation for Markov decision processes , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.