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» Distributed Control by Lagrangian Steepest Descent
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CORR
2004
Springer
91views Education» more  CORR 2004»
13 years 4 months ago
Distributed Control by Lagrangian Steepest Descent
Often adaptive, distributed control can be viewed as an iterated game between independent players. The coupling between the players' mixed strategies, arising as the system ev...
David Wolpert, Stefan Bieniawski
ICDCS
2010
IEEE
13 years 8 months ago
Stochastic Steepest-Descent Optimization of Multiple-Objective Mobile Sensor Coverage
—We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the ...
Chris Y. T. Ma, David K. Y. Yau, Nung Kwan Yip, Na...
ATAL
2004
Springer
13 years 10 months ago
Product Distribution Theory for Control of Multi-Agent Systems
Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MAS’s). First we review one motivation of PD theory, as the information-theoretic extens...
Chiu Fan Lee, David H. Wolpert
WCNC
2008
IEEE
13 years 11 months ago
On the Distribution of Positioning Errors in Wireless Sensor Networks: A Simulative Comparison of Optimization Algorithms
Abstract—Recent advances in the technology of wireless electronic devices have made possible to build ad–hoc Wireless Sensor Networks (WSNs) using inexpensive nodes consisting ...
Stefano Tennina, Marco Di Renzo, Fortunato Santucc...
CDC
2009
IEEE
132views Control Systems» more  CDC 2009»
13 years 9 months ago
Q-learning and Pontryagin's Minimum Principle
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
Prashant G. Mehta, Sean P. Meyn