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NIPS
1993
13 years 6 months ago
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms,includ...
Tommi Jaakkola, Michael I. Jordan, Satinder P. Sin...
CDC
2010
IEEE
139views Control Systems» more  CDC 2010»
12 years 11 months ago
Q-learning and enhanced policy iteration in discounted dynamic programming
We consider the classical finite-state discounted Markovian decision problem, and we introduce a new policy iteration-like algorithm for finding the optimal state costs or Q-facto...
Dimitri P. Bertsekas, Huizhen Yu
ORL
2008
115views more  ORL 2008»
13 years 4 months ago
On the convergence of stochastic dual dynamic programming and related methods
We discuss the almost-sure convergence of a broad class of sampling algorithms for multi-stage stochastic linear programs. We provide a convergence proof based on the finiteness o...
Andrew B. Philpott, Z. Guan
IOR
2010
98views more  IOR 2010»
13 years 2 months ago
A Shadow Simplex Method for Infinite Linear Programs
We present a Simplex-type algorithm, that is, an algorithm that moves from one extreme point of the infinite-dimensional feasible region to another not necessarily adjacent extrem...
Archis Ghate, Dushyant Sharma, Robert L. Smith
CCE
2004
13 years 4 months ago
Improving convergence of the stochastic decomposition algorithm by using an efficient sampling technique
This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-st...
José María Ponce-Ortega, Vicente Ric...