Abstract
In order to find a control policy for an autonomous robot by reinforcement learning, the utility of a behaviour can be revealed locally through a modulation of the motor command by probing actions. For robots with many degrees of freedom, this type of exploration becomes inefficient such that it is an interesting option to use an auxiliary controller for the selection of promising probing actions. We suggest here to optimise the exploratory modulation by a self-organising controller. The approach is illustrated by two control tasks, namely swing-up of a pendulum and walking in a simulated hexapod. The results imply that the homeokinetic approach is beneficial for high complexity problems.
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
Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man and Cybernetics 13, 834–846 (1983)
Der, R.: Self-organized acquisition of situated behavior. Theory Biosci. 120, 179–187 (2001)
Der, R., Michael Herrmann, J., Liebscher, R.: Homeokinetic approach to autonomous learning in mobile robots. VDI-Berichte, vol. 1679, pp. 301–306 (2002)
Der, R., Liebscher, R.: True autonomy from self-organized adaptivity. In: Workshop Biologically Inspired Robotics, Bristol (2002)
Doya, K.: Reinforcement learning in continuous time and space. Neural Computation 12, 219–245 (2000)
Ekeberg, Ö., Blümel, M., Büschges, A.: Dynamic simulation of insect walking. Arthropod Structure & Development 33, 287–300 (2004)
Gullapalli, V.: A stochastic reinforcement learning algorithm for learning real-valued functions. Neural Networks 3, 671–692 (1990)
Martius, G.: Goal-Oriented Control of Self-Organizing Behavior in Autonomous Robots. PhD thesis, Göttingen University (2010)
Martius, G., Herrmann, J.M.: Tipping the scales: Guidance and intrinsically motivated behavior. In: Proc. of Europ. Conf. on Artificial Life (2011)
Martius, G., Herrmann, J.M., Der, R.: Guided Self-Organisation for Autonomous Robot Development. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) ECAL 2007. LNCS (LNAI), vol. 4648, pp. 766–775. Springer, Heidelberg (2007)
Martius, G., Hesse, F., Güttler, F., Der, R.: Lpzrobots: A free and powerful robot simulator (2011), robot.informatik.uni-leipzig.de
Sutton, R.S.: Learning to predict by the methods of temporal differences. Machine Learning 3, 9–44 (1988)
Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. MIT Press, Cambridge (1998); A Bradford Book
Wiener, N.: Cybernetics or Control and Communication in the Animal and the Machine. Hermann, Paris (1948)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Smith, S.C., Herrmann, J.M. (2012). Homeokinetic Reinforcement Learning. In: Schwenker, F., Trentin, E. (eds) Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science(), vol 7081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28258-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-28258-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28257-7
Online ISBN: 978-3-642-28258-4
eBook Packages: Computer ScienceComputer Science (R0)