An extension of the rational policy making algorithm to continuous state spaces

[1]  Andrew Y. Ng,et al.  Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping , 1999, ICML.

[2]  Pieter Abbeel,et al.  Exploration and apprenticeship learning in reinforcement learning , 2005, ICML.

[3]  Shigenobu Kobayashi,et al.  An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function , 1998, ICML.

[4]  S. Kobayashi,et al.  Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[5]  Shigenobu Kobayashi,et al.  An Extension of Profit Sharing to Partially Observable Markov Decision Processes: Proposition of PS-r* and its Evaluation , 2003 .

[6]  H. Kimura.  Reinforcement Learning in Multi-dimensional State-action Space Using Random Tiling and Gibbs Sampling , 2006 .

[7]  Lonnie Chrisman,et al.  Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach , 1992, AAAI.

[8]  Osamu Katai,et al.  Fuzzy Interpolation-Based Q-Learning with Continuous Inputs and Outputs , 1999 .

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

[10]  Andrew Y. Ng,et al.  Pharmacokinetics of a novel formulation of ivermectin after administration to goats , 2000, ICML.

[11]  Richard S. Sutton,et al.  Learning to predict by the methods of temporal differences , 1988, Machine Learning.

[12]  T. Tateyama,et al.  A Reinforcement Learning Algorithm for Continuous State Spaces using Multiple Fuzzy-ART Networks , 2006, 2006 SICE-ICASE International Joint Conference.