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SIAMCO
2000
117views more  SIAMCO 2000»
13 years 9 months ago
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Vivek S. Borkar, Sean P. Meyn
ICASSP
2011
IEEE
13 years 1 months ago
Reinforcement learning for energy-efficient wireless transmission
We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. We propose a rigorous and unified fra...
Nicholas Mastronarde, Mihaela van der Schaar
CAEPIA
2011
Springer
12 years 10 months ago
Evaluating a Reinforcement Learning Algorithm with a General Intelligence Test
In this paper we apply the recent notion of anytime universal intelligence tests to the evaluation of a popular reinforcement learning algorithm, Q-learning. We show that a general...
Javier Insa-Cabrera, David L. Dowe, José He...
NN
2007
Springer
105views Neural Networks» more  NN 2007»
13 years 9 months ago
Guiding exploration by pre-existing knowledge without modifying reward
Reinforcement learning is based on exploration of the environment and receiving reward that indicates which actions taken by the agent are good and which ones are bad. In many app...
Kary Främling
JSAC
2010
107views more  JSAC 2010»
13 years 8 months ago
Online learning in autonomic multi-hop wireless networks for transmitting mission-critical applications
Abstract—In this paper, we study how to optimize the transmission decisions of nodes aimed at supporting mission-critical applications, such as surveillance, security monitoring,...
Hsien-Po Shiang, Mihaela van der Schaar