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» Localizing Search in Reinforcement Learning
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UAI
2008
13 years 11 months ago
Improving Gradient Estimation by Incorporating Sensor Data
An efficient policy search algorithm should estimate the local gradient of the objective function, with respect to the policy parameters, from as few trials as possible. Whereas m...
Gregory Lawrence, Stuart J. Russell
ECAI
2004
Springer
14 years 3 months ago
Improving Asynchronous Backtracking for Dealing with Complex Local Problems
Distributed constraint satisfaction, in its most general acceptation, involves a collection of agents solving local constraint satisfaction subproblems, and a communication protoco...
Arnold Maestre, Christian Bessière
CLEF
2010
Springer
13 years 10 months ago
Visual Localization Using Global Visual Features and Vanishing Points
Abstract. This paper describes a visual localization approach for mobile robots. Robot localization is performed as location recognition. The approach uses global visual features (...
Olivier Saurer, Friedrich Fraundorfer, Marc Pollef...
GECCO
2010
Springer
158views Optimization» more  GECCO 2010»
14 years 1 months ago
Efficiently evolving programs through the search for novelty
A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic pro...
Joel Lehman, Kenneth O. Stanley
ICML
2007
IEEE
14 years 10 months ago
Best of both: a hybridized centroid-medoid clustering heuristic
Although each iteration of the popular kMeans clustering heuristic scales well to larger problem sizes, it often requires an unacceptably-high number of iterations to converge to ...
Nizar Grira, Michael E. Houle