Stochastic dynamic programming with factored representations
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Planning under uncertainty in complex structured environments
Planning under uncertainty in complex structured environments
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
Mean Field Approximation of the Policy Iteration Algorithm for Graph-based Markov Decision Processes
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Mean Field Approximation of the Policy Iteration Algorithm for Graph-based Markov Decision Processes
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
A framework and a mean-field algorithm for the local control of spatial processes
International Journal of Approximate Reasoning
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In this article, we consider a form of compact representation of MDP based on graphs, and we propose an approximate solution algorithm derived from this representation. The approach we propose belongs to the family of Approximate Linear Programming methods, but the graph-structure we assume allows it to become particularly efficient. The proposed method complexity is linear in the number of variables in the graph and only exponential in the width of a dependency graph among variables.