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NIPS
1993
15 years 7 months ago
Robust Reinforcement Learning in Motion Planning
While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...
Satinder P. Singh, Andrew G. Barto, Roderic A. Gru...
ICRA
2006
IEEE
91views Robotics» more  ICRA 2006»
16 years 17 days ago
Minimum Wheel-rotation Paths for Differential-drive Mobile Robots
— Characterizing optimal paths for mobile robots is an interesting, important, and challenging endeavor. Not only they are interesting with respect to the optimized criteria, but...
Hamid Reza Chitsaz, Steven M. LaValle, Devin J. Ba...
CORR
2008
Springer
91views Education» more  CORR 2008»
15 years 6 months ago
Randomised Variable Neighbourhood Search for Multi Objective Optimisation
Various local search approaches have recently been applied to machine scheduling problems under multiple objectives. Their foremost consideration is the identification of the set ...
Martin Josef Geiger
EOR
2010
116views more  EOR 2010»
15 years 6 months ago
Speeding up continuous GRASP
Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hi...
Michael J. Hirsch, Panos M. Pardalos, Mauricio G. ...
ICCV
2003
IEEE
16 years 8 months ago
Globally Convergent Autocalibration
Existing autocalibration techniques use numerical optimization algorithms that are prone to the problem of local minima. To address this problem, we have developed a method where ...
Arrigo Benedetti, Alessandro Busti, Michela Farenz...