Abstract
An emergence of intelligent behavior within a simple robotic agent is studied in this paper. Two control mechanisms for an agent are considered — a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforcement learning algorithm over a finite agent state space. A comparison of these two approaches is presented on the maze exploration problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321–355 (1988)
E-puck, online documentation, http://www.e-puck.org
Fogel, D.B.: Evolutionary Computation: The Fossil Record. MIT/ IEEE Press (1998)
Haykin, S.: Neural Networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Holland, J.: Adaptation In Natural and Artificial Systems. MIT Press, Cambridge (1992)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Moody, J., Darken, C.: Fast learning in networks of locally-tuned processing units. Neural Computation 1, 289–303 (1989)
Nolfi, S., Floreano, D.: Evolutionary Robotics — The Biology, Intelligence and Techology of Self-Organizing Machines. MIT Press, Cambridge (2000)
Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (2000)
Poggio, T., Girosi, F.: A theory of networks for approximation and learning. Technical report, Massachusetts Institute of Technology, Cambridge, MA, USA, A. I. Memo No. 1140, C.B.I.P. Paper No. 31 (1989)
Slušný, S., Neruda, R.: Evolving homing behaviour for team of robots. In: Computational Intelligence, Robotics and Autonomous Systems. Massey University, Palmerston North (2007)
Slušný, S., Neruda, R., Vidnerová, P.: Evolution of simple behavior patterns for autonomous robotic agent. In: System Science and Simulation in Engineering, pp. 411–417. WSEAS Press (2007)
Richard Sutton, S., Andrew Barto, G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Watkins, C.J.C.H.: Learning from delayed rewards. Ph.D. thesis (1989)
Webots simulator. On-line documentation, http://www.cyberbotics.com/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Slušný, S., Neruda, R., Vidnerová, P. (2008). Comparison of RBF Network Learning and Reinforcement Learning on the Maze Exploration Problem. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_74
Download citation
DOI: https://doi.org/10.1007/978-3-540-87536-9_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87535-2
Online ISBN: 978-3-540-87536-9
eBook Packages: Computer ScienceComputer Science (R0)