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» Coding discretizations of continuous functions
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NIPS
2003
13 years 9 months ago
Gaussian Processes in Reinforcement Learning
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...
Carl Edward Rasmussen, Malte Kuss
JAIR
2006
122views more  JAIR 2006»
13 years 7 months ago
Solving Factored MDPs with Hybrid State and Action Variables
Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automa...
Branislav Kveton, Milos Hauskrecht, Carlos Guestri...
TSP
2008
178views more  TSP 2008»
13 years 7 months ago
Digital Computation of Linear Canonical Transforms
Abstract--We deal with the problem of efficient and accurate digital computation of the samples of the linear canonical transform (LCT) of a function, from the samples of the origi...
A. Koc, Haldun M. Özaktas, Cagatay Candan, M....
WSC
2000
13 years 9 months ago
A practical approach to sample-path simulation optimization
We propose solving continuous parametric simulation optimizations using a deterministic nonlinear optimization algorithm and sample-path simulations. The optimization problem is w...
Michael C. Ferris, Todd S. Munson, Krung Sinapirom...
BMCBI
2002
131views more  BMCBI 2002»
13 years 7 months ago
Efficient Boolean implementation of universal sequence maps (bUSM)
Background: Recently, Almeida and Vinga offered a new approach for the representation of arbitrary discrete sequences, referred to as Universal Sequence Maps (USM), and discussed ...
John Schwacke, Jonas S. Almeida