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SYNASC
2005
IEEE
97views Algorithms» more  SYNASC 2005»
14 years 3 months ago
A Reinforcement Learning Algorithm for Spiking Neural Networks
The paper presents a new reinforcement learning mechanism for spiking neural networks. The algorithm is derived for networks of stochastic integrate-and-fire neurons, but it can ...
Razvan V. Florian
ICDM
2002
IEEE
105views Data Mining» more  ICDM 2002»
14 years 3 months ago
Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing
We empirically evaluate the performance of various reinforcement learning methods in applications to sequential targeted marketing. In particular, we propose and evaluate a progre...
Naoki Abe, Edwin P. D. Pednault, Haixun Wang, Bian...
KBS
2006
105views more  KBS 2006»
13 years 10 months ago
Robot docking based on omnidirectional vision and reinforcement learning
We present a system for visual robotic docking using an omnidirectional camera coupled with the actor critic reinforcement learning algorithm. The system enables a PeopleBot robot...
David Muse, Cornelius Weber, Stefan Wermter
ICRA
1994
IEEE
94views Robotics» more  ICRA 1994»
14 years 2 months ago
A Reinforcement-Learning Approach to Reactive Control Policy Design for Autonomous Robots
Within the field of robotics, much recent attention has been given to control techniques that have been termed reactive or behavior-based. The design of such control systems for e...
Andrew H. Fagg, David Lotspeich, George A. Bekey
FSTTCS
2006
Springer
14 years 1 months ago
Testing Probabilistic Equivalence Through Reinforcement Learning
We propose a new approach to verification of probabilistic processes for which the model may not be available. We use a technique from Reinforcement Learning to approximate how far...
Josee Desharnais, François Laviolette, Sami...