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ICML
2000
IEEE
14 years 8 months ago
Rates of Convergence for Variable Resolution Schemes in Optimal Control
This paper presents a general method to derive tight rates of convergence for numerical approximations in optimal control when we consider variable resolution grids. We study the ...
Andrew W. Moore, Rémi Munos
UAI
2008
13 years 9 months ago
Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
Richard S. Sutton, Csaba Szepesvári, Alborz...
FOGA
2011
12 years 11 months ago
Approximating the distribution of fitness over hamming regions
The distribution of fitness values across a set of states sharply influences the dynamics of evolutionary processes and heuristic search in combinatorial optimization. In this p...
Andrew M. Sutton, Darrell Whitley, Adele E. Howe
ICPR
2006
IEEE
14 years 8 months ago
Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network
To accelerate the learning of reinforcement learning, many types of function approximation are used to represent state value. However function approximation reduces the accuracy o...
Siwei Luo, Yu Zheng, Ziang Lv
ICML
1996
IEEE
14 years 8 months ago
Learning Evaluation Functions for Large Acyclic Domains
Some of the most successful recent applications of reinforcement learning have used neural networks and the TD algorithm to learn evaluation functions. In this paper, we examine t...
Justin A. Boyan, Andrew W. Moore