Publication: Applying evolution strategies to neural networks robot controller
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1999
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Springer
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Abstract
In this paper an evolution strategy (ES) is introduced, to learn weights of a neural network controller in autonomous robots. An ES is used to learn high-performance reactive behavior for navigation and collisions avoidance. The learned behavior is able to solve the problem in different environments; so, the learning process has proven the ability to obtain a specialized behavior. All the behaviors obtained have been tested in a set of environment and the capability of generalization is showed for each learned behavior. No subjective information about “how to accomplish the task” has been included in the fitness function. A simulator based on mini-robot Khepera has been used to learn each behavior.
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Proceeding of: International Work-Conference on Artificial and Natural Neural Networks, IWANN'99 Alicante, Spain, June 2–4, 1999
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Engineering applications of bio-inspired artificial neural networks. Berlin: Springer, 1999. p. 516-525 (Lecture Notes in Computer Science; 1607)