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ICML
2010
IEEE
8 years 10 months 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...
NIPS
2003
8 years 10 months ago
Linear Program Approximations for Factored Continuous-State Markov Decision Processes
Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with finite state spaces. In this work we show th...
Milos Hauskrecht, Branislav Kveton
UAI
2008
8 years 10 months ago
Partitioned Linear Programming Approximations for MDPs
Approximate linear programming (ALP) is an efficient approach to solving large factored Markov decision processes (MDPs). The main idea of the method is to approximate the optimal...
Branislav Kveton, Milos Hauskrecht
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