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ICPR
2006
IEEE
15 years 11 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»
15 years 3 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...
ECAL
2005
Springer
15 years 3 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
15 years 4 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
IROS
2007
IEEE
144views Robotics» more  IROS 2007»
15 years 4 months ago
Using reinforcement learning to adapt an imitation task
Abstract— The goal of developing algorithms for programming robots by demonstration is to create an easy way of programming robots that can be accomplished by everyone. When a de...
Florent Guenter, Aude Billard