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ICPR
2006
IEEE
14 years 9 months ago
Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network
To accelerate the learning of reinforcement learning, many types of function approximation are used to represent state value. However function approximation reduces the accuracy o...
Siwei Luo, Yu Zheng, Ziang Lv
ICRA
2005
IEEE
140views Robotics» more  ICRA 2005»
14 years 1 months ago
Fast Reinforcement Learning for Vision-guided Mobile Robots
— This paper presents a new reinforcement learning algorithm for accelerating acquisition of new skills by real mobile robots, without requiring simulation. It speeds up Q-learni...
Tomás Martínez-Marín, Tom Duc...
WSDM
2012
ACM
214views Data Mining» more  WSDM 2012»
12 years 3 months ago
Selecting actions for resource-bounded information extraction using reinforcement learning
Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to d...
Pallika H. Kanani, Andrew K. McCallum
ECAL
2005
Springer
14 years 1 months ago
The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...
J. J. McDowell, Zahra Ansari
CEC
2008
IEEE
14 years 2 months ago
Creating edge detectors by evolutionary reinforcement learning
— In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is c...
Nils T. Siebel, Sven Grünewald, Gerald Sommer