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ICML
1996
IEEE
14 years 25 days ago
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning
This paper presents a direct reinforcement learning algorithm, called Finite-Element Reinforcement Learning, in the continuous case, i.e. continuous state-space and time. The eval...
Rémi Munos
JMLR
2006
124views more  JMLR 2006»
13 years 8 months ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
ATAL
2005
Springer
14 years 2 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
GECCO
2008
Springer
177views Optimization» more  GECCO 2008»
13 years 9 months ago
Reduced computation for evolutionary optimization in noisy environment
Evolutionary Algorithms’ (EAs’) application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on ...
Maumita Bhattacharya
WISTP
2007
Springer
14 years 2 months ago
Mobile Phones as Secure Gateways for Message-Based Ubiquitous Communication
Abstract. For ubiquitous communication self-organising adhoc networks become more and more important. We consider mobile phones as appropriate secure gateways to provide access to ...
Walter Bamberger, Oliver Welter, Stephan Spitz