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» Adaptive control of constrained finite Markov chains
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NIPS
1998
13 years 8 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
ICDCS
2010
IEEE
13 years 11 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...
JPDC
2007
209views more  JPDC 2007»
13 years 7 months ago
Application-aware integration of data collection and power management in wireless sensor networks
Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysi...
Qi Han, Sharad Mehrotra, Nalini Venkatasubramanian