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.
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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/
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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
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DOI: https://doi.org/10.1007/978-3-540-87536-9_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87535-2
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