Stable Function Approximation in Dynamic Programming

[1]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[2]  Benjamin Van Roy,et al.  Feature-based methods for large scale dynamic programming , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[3]  Michael I. Jordan,et al.  MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES , 1996 .

[4]  Andrew W. Moore,et al.  Generalization in Reinforcement Learning: Safely Approximating the Value Function , 1994, NIPS.

[5]  A. Moore Variable Resolution Dynamic Programming , 1991, ML.

[6]  Gerald Tesauro,et al.  Neurogammon: a neural-network backgammon program , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[7]  Toshio Odanaka,et al.  ADAPTIVE CONTROL PROCESSES , 1990 .

[8]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[9]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[10]  C. Watkins Learning from delayed rewards , 1989 .

[11]  John N. Tsitsiklis,et al.  An optimal multigrid algorithm for discrete-time stochastic control , 1989 .

[12]  Ian H. Witten,et al.  An Adaptive Optimal Controller for Discrete-Time Markov Environments , 1977, Inf. Control..

[13]  R. A. Silverman,et al.  Introductory Real Analysis , 1972 .

[14]  D. Blackwell Discounted Dynamic Programming , 1965 .

[15]  R. Bellman,et al.  Polynomial approximation—a new computational technique in dynamic programming: Allocation processes , 1962 .

[16]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[17]  R. Bellman,et al.  FUNCTIONAL APPROXIMATIONS AND DYNAMIC PROGRAMMING , 1959 .