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ICML
1994
IEEE
14 years 17 days ago
A Modular Q-Learning Architecture for Manipulator Task Decomposition
Compositional Q-Learning (CQ-L) (Singh 1992) is a modular approach to learning to performcomposite tasks made up of several elemental tasks by reinforcement learning. Skills acqui...
Chen K. Tham, Richard W. Prager
ATAL
2008
Springer
13 years 11 months ago
Approximate predictive state representations
Predictive state representations (PSRs) are models that represent the state of a dynamical system as a set of predictions about future events. The existing work with PSRs focuses ...
Britton Wolfe, Michael R. James, Satinder P. Singh
NIPS
2001
13 years 10 months ago
Model-Free Least-Squares Policy Iteration
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Michail G. Lagoudakis, Ronald Parr
AIWORC
2000
IEEE
14 years 1 months ago
Distance Learning Using Web-Based Multimedia Environment
The "schooling industry" is faced with an inescapable demand to redefine its endeavors in terms of producing learning, rather than providing instructions. We propose a h...
Khalid J. Siddiqui, Junaid Ahmed Zubairi
DIS
2009
Springer
14 years 3 months ago
OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers
Abstract. Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) boilers. If control systems fail to compensate the ï¬...
Indre Zliobaite, Jorn Bakker, Mykola Pechenizkiy