Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Understanding intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Fast learning in networks of locally-tuned processing units
Neural Computation
Evolution of simple behavior patterns for autonomous robotic agent
ICOSSSE'07 Proceedings of the 6th WSEAS international conference on System science and simulation in engineering
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We study behavioural patterns learned by a robotic agent by means of two different control and adaptive approaches -- a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforcement Q-learning algorithm. In both cases, a set of rules controlling the agent is derived from the learned controllers, and these sets are compared. It is shown that both procedures lead to reasonable and compact, albeit rather different, rule sets.