Abstract
In this work we describe the design of a control approach in which, by way of supervised reinforcement learning, the learning potential is combined with the previous knowledge of the task in question, obtaining as a result rapid convergence to the desired behaviour as well as an increase in the stability of the process. We have tested the application of our approach in the design of a basic behaviour pattern in mobile robotics, such as that of wall following. We have carried out several experiments obtaining goods results which confirm the utility and advantages derived from the use of our approach.
This work has been possible thanks to Xunta de Galicia, project XUGA20608B, and thanks to the availability of a Nomad200 mobile robot acquired through a infraestructure project funded by Xunta de Galicia.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
P. Cichosz. Reinforcement learning algorithms based on the methods of temporal differences. Master's thesis, Warsaw University of Technology, Septeniber 1994.
D. Fox, W. Burgard, and S. Thrun. The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 4(1):23–33, 1997.
R. Iglesias, C.V. Regueiro, J. Correa, and S. Barro. Implementation of a basic reactive behavior in mobile robotics through artificial neural networks. In Proc. of IWANN'97, 1997.
R. Iglesias, C.V. Regueiro, J. Correa, E. Sánchez, and S. Barro. Improving wall following lwhaviour in a mobile rol)ot using reinforcement learning. In Proceedings of the Iraternational ICSC Symposium on Engineering of Intelligent Systems, 1998.
R. Garcia J. Gasós, M.C. Garcia-Alegre. Fuzzy strategies for the navigation of autonomous mobile robots. In Proc. of IFES'91, pages 1024–1034, 1991.
R. Maclin and J.W. Shavlik. Creating advice-taking reinforcement learners. Machine, Learning, 22:251–281,1996.
Y. Smirnov, S. Koenig, M.M. Veloso, and R.G. Simmons. Efficient goal-directed exploration. In Proceedings of the Thirtenth National Conference on Artificial Intelligence, pages 292–297, 1996.
R. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3:9–44, 1988.
C. Watkins. Learning from Delayed Reruards. PhD thesis, King's College, Cambridge, 1989.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iglesias, R., Regueiro, C.V., Correa, J., Barro, S. (1998). Supervised reinforcement learning: Application to a wall following behaviour in a mobile robot. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_416
Download citation
DOI: https://doi.org/10.1007/3-540-64574-8_416
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64574-0
Online ISBN: 978-3-540-69350-5
eBook Packages: Springer Book Archive