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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
15 years 2 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
CDC
2010
IEEE
146views Control Systems» more  CDC 2010»
14 years 11 months ago
Adaptive autonomous soaring of multiple UAVs using Simultaneous Perturbation Stochastic Approximation
Abstract-- This paper presents a new algorithm for maximizing the flight duration of a single UAV (Uninhabited Air Vehicle) and UAVs group using the thermal model developed by Alle...
Cathrine Antal, Oleg Granichin, Sergey Levi
CDC
2010
IEEE
105views Control Systems» more  CDC 2010»
14 years 11 months ago
Universal scheduling for networks with arbitrary traffic, channels, and mobility
We extend stochastic network optimization theory to treat networks with arbitrary sample paths for arrivals, channels, and mobility. The network can experience unexpected link or n...
Michael J. Neely
CDC
2010
IEEE
107views Control Systems» more  CDC 2010»
14 years 11 months ago
Time-synchronization in mobile sensor networks from difference measurements
We examine distributed time-synchronization in mobile ad-hoc and sensor networks. The problem is to estimate the skews and offsets of clocks of all the nodes with respect to an arb...
Chenda Liao, Prabir Barooah
CDC
2010
IEEE
196views Control Systems» more  CDC 2010»
14 years 11 months ago
Convergence and convergence rate of stochastic gradient search in the case of multiple and non-isolated extrema
The asymptotic behavior of stochastic gradient algorithms is studied. Relying on some results of differential geometry (Lojasiewicz gradient inequality), the almost sure pointconve...
Vladislav B. Tadic