Abstract
The final goal of this research is to realize a small airship robot that can automatically achieve a given task. The airship is subjected to strong inertial forces and air resistance. Although reinforcement learning methods could be expected to control a small airship, the unstable property of the airship prevents the learning methods from achieving control of it.
In order to design an automatically controlled airship, sensory information is especially important. We assume using like ultrasonic transducers which have been widely used as a cheap and light way to provide mobile robots with accurate range finders. This paper verifies the difference in control performance of the airship between a variety of sensory setup. We simulated a small airship with the Cerebellar Model Articulation Controller (CMAC) as a reinforcement learning method which is enabled to deal with generalization problems, on the assumption that we use ultrasonic transducers afterward.
The experimental results showed that the learning performance was not always proportional to the amount of sensory information, and different behavior was acquired according to differences in the sensory setup.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lin, L. -J. Scaling Up Reinforcement Learning for Robot Control. Proceedings of the 10th International Conference on Machine Learning 1993: 182–189
H. Murao, I. Kitagawa and S. Kitamura. Adaptive State Segmentation for Q-learning (in Japanese). Proceedings of SICE00′97 1997: 45–48
N. Ono and Y. Fukuta. Learning to Coordinate in a Continuous Environment. Proceedings of the Second International Conference on Multi-agent Systems, 1996
Singh, S. P. Reinforcement Learning with Replacing Eligibility Traces. Machine Learning 1996;22,l/2/3: 123–158
Sutton, R. S. Learning to predict by the methods of temporal differences. Machine Learning 1988;3: 9–44
Sutton, R. S. Generalization in reinforcement learning: Successful examples using sparce coarse coding. In D.S.Touretzky, M.C.Mozer and M.E.Hasselmo(eds.), Advances in Neural Information Processing Systems 1996: 1038–1044
Y. Takahashi, M. Asada and K. Hosoda. Reasonable Performance in Less Learning Time by Real Robot Based on Incremental State Space Segmentation. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 1996: 1038–1044
Ming Tan. Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents. Proceedings of the Tenth International Conference on Machine Learning 1993: 330–337
Wiering, M. and Schmidhuber, J. Speeding up Q(λ)-learning. Proceedings of the Tenth European Conference on Machine Learning 1998: 352–363
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media New York
About this chapter
Cite this chapter
Motoyama, K., Suzuki, K., Kawamura, H., Yamamoto, M., Ohuchi, A. (2002). Design of Adaptive Self-Navigated Airship in Simulated Environment. In: Kozan, E., Ohuchi, A. (eds) Operations Research/Management Science at Work. International Series in Operations Research & Management Science, vol 43. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0819-9_13
Download citation
DOI: https://doi.org/10.1007/978-1-4615-0819-9_13
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5254-9
Online ISBN: 978-1-4615-0819-9
eBook Packages: Springer Book Archive