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NIPS
2007
13 years 10 months ago
Random Sampling of States in Dynamic Programming
We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using lo...
Christopher G. Atkeson, Benjamin Stephens
GECCO
2007
Springer
235views Optimization» more  GECCO 2007»
14 years 3 months ago
Expensive optimization, uncertain environment: an EA-based solution
Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function e...
Maumita Bhattacharya
ECML
2006
Springer
14 years 17 days ago
Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
Sébastien Jodogne, Cyril Briquet, Justus H....
COMPUTING
2004
204views more  COMPUTING 2004»
13 years 8 months ago
Image Registration by a Regularized Gradient Flow. A Streaming Implementation in DX9 Graphics Hardware
The presented image registration method uses a regularized gradient flow to correlate the intensities in two images. Thereby, an energy functional is successively minimized by des...
Robert Strzodka, Marc Droske, Martin Rumpf
PE
1998
Springer
158views Optimization» more  PE 1998»
13 years 8 months ago
Asymptotic Approximations and Bottleneck Analysis in Product Form Queueing Networks with Large Populations
Asymptotic approximations are constructed for the performance measures of product form queueing networks consisting of single server, fixed rate nodes with large populations. The...
Charles Knessl, Charles Tier