NDLI: Combine and compare evolutionary robotics and reinforcement Learning as methods of designing autonomous robots

Content Provider	IEEE Xplore Digital Library
Author	Goschin, S. Franti, E. Dascalu, M. Osiceanu, S.
Copyright Year	2007
Description	Author affiliation: Univ. Politehnica Bucharest, Bucharest (Goschin, S.)
Abstract	The purpose of this paper is to present a comparison between two methods of building adaptive controllers for robots. In spite of the wide range of techniques which are used for defining autonomous robot architectures, few attempts have been made in comparing their performance under similar circumstances. This comparison is particularly important in establishing benchmarks and in determining the best approach methods. The robotic tasks in our research concern mainly the convergence of behaviors like obstacle avoidance, hitting targets and shortest path finding using various methods of synthesizing control architectures' parameters. The first approach that has been used combines Neural Networks and Genetic Algorithms in a simple yet robust controller using an Evolutionary Robotics technique. The second one introduces a manner of using Reinforcement Learning with a Neural Network based architecture. The experiments take place in a simulated 3D environment, which was designed to allow the development, testing and comparison of various controllers in terms of advantages and disadvantages in order to establish a benchmark for autonomous robots.
Starting Page	1511
Ending Page	1516
File Size	231946
Page Count	6
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424651
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Design methodology Neural networks Buildings Programmable control Adaptive control Robot control Convergence Network synthesis Genetic algorithms
Content Type	Text
Resource Type	Article

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