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ATAL
2010
Springer
13 years 6 months ago
Approximate dynamic programming with affine ADDs
The Affine ADD (AADD) is an extension of the Algebraic Decision Diagram (ADD) that compactly represents context-specific, additive and multiplicative structure in functions from a...
Scott Sanner, William T. B. Uther, Karina Valdivia...
EMNLP
2009
13 years 9 months ago
Feature-Rich Translation by Quasi-Synchronous Lattice Parsing
We present a machine translation framework that can incorporate arbitrary features of both input and output sentences. The core of the approach is a novel decoder based on lattice...
Kevin Gimpel, Noah A. Smith
TSMC
2008
140views more  TSMC 2008»
13 years 11 months ago
Adaptive Feedback Control by Constrained Approximate Dynamic Programming
A constrained approximate dynamic programming (ADP) approach is presented for designing adaptive neural network (NN) controllers with closed-loop stability and performance guarante...
S. Ferrari, J. E. Steck, R. Chandramohan
CORR
2010
Springer
119views Education» more  CORR 2010»
13 years 11 months ago
Dynamic Policy Programming
In this paper, we consider the problem of planning and learning in the infinite-horizon discounted-reward Markov decision problems. We propose a novel iterative direct policysearc...
Mohammad Gheshlaghi Azar, Hilbert J. Kappen
ICML
2010
IEEE
14 years 17 days ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...
ATMOS
2007
177views Optimization» more  ATMOS 2007»
14 years 1 months ago
Approximate dynamic programming for rail operations
Abstract. Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled...
Warren B. Powell, Belgacem Bouzaïene-Ayari
IJCNN
2006
IEEE
14 years 5 months ago
Cellular SRN Trained by Extended Kalman Filter Shows Promise for ADP
— Cellular simultaneous recurrent neural network has been suggested to be a function approximator more powerful than the MLP’s, in particular for solving approximate dynamic pr...
Roman Ilin, Robert Kozma, Paul J. Werbos
CDC
2008
IEEE
206views Control Systems» more  CDC 2008»
14 years 6 months ago
Approximate dynamic programming using support vector regression
— This paper presents a new approximate policy iteration algorithm based on support vector regression (SVR). It provides an overview of commonly used cost approximation architect...
Brett Bethke, Jonathan P. How, Asuman E. Ozdaglar