Abstract
The paper presents a way of extending complementary reinforcement backpropagation learning (CRBP) to modular architectures using a new version of the gating network approach in the context of reactive navigation tasks for a simulated mobile robot. The gating network has partially recurrent connections to enable the co-ordination of reinforcement learning across both modules successive time steps. The experiments reported explore the possibility that architectures based on this approach can support concurrent acquisition of different reactive navigation related competences while the robot pursues light-seeking goals.
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.
References
D. H. Ackley and M. L. Littman. Generalisation and scaling in reinforcement learning. In D. S. Touretsky, editor, Advances in Neural Information Processing Systems, pages 550–557. Morgan Kaufmann, San Mateo, CA, 1990.
R. A. Brooks. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, RA-2:14–23, 1986.
R. A. Brooks. Intelligence without representation. Artificial Intelligence, 47:131–159, 1991.
J. Elman. Finding structure in time. Cognitive Science, 14:179–192, 1990.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3:337–345, 1991.
L. Meeden, G. McGraw, and D. Blank. Emergent control and planning in an autonomous vehicle. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society, 1994.
R. Molland, T. Scutt, and P. Green. Extending low-level reactive behaviours using primitive behavioural memory. In Proceedings of the International Conference on Recent Advances in Mechatronics, pages 510–516, 1995.
R. M. Rylatt, C. A. Czaxnecki, and T. W. Routen. Connectionist learning in behaviour-based mobile robots: A survey. In Artificial Intelligence Review. Kluwer Academic Publishers. (to appear).
R. M. Rylatt, C. A. Czarnecki, and T. W. Routen. A perspective on the future of behaviour-based robotics. In Mobile Robotics Workshop Notes — Tenth Biennial Conference on Artificial Intelligence and Simulated Behaviour, 1995.
R. M. Rylatt, C. A. Czarnecki, and T. W. Routen. Learning behaviours in a modular neural net architecture for a mobile autonomous agent. In Proceedings of the First Euromicro Workshop on Advanced Mobile Robots, pages 82–86, 1996.
G. M. Saunders, J. F. Kolen, and J. B. Pollack. The importance of leaky levels for behaviour based A.I. In From Animals to Animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour, pages 275–281. MIT Press, 1994.
R. J. Williams. On the use of backpropagation in associative reinforcement learning. In Proceedings of the IEEE International Conference on Neural Networks, pages 263–270, 1988.
T. Ziemke. Towards adaptive perception in autonomous robots using second-order recurrent networks. In Proceedings of the First Euromicro Workshop on Advanced Mobile Robots, pages 89–98, 1996.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Rylatt, R.M., Czarnecki, C.A., Routen, T.W. (1998). A Modular Reinforcement Learning Architecture for Mobile Robot Control. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6492-1_2
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive