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ICML
1996
IEEE
15 years 10 months 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»
15 years 6 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
15 years 11 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»
15 years 7 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
16 years 2 days 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