- Poster presentation
- Open Access
Reinforcement learning on complex visual stimuli
© Wilbert et al; licensee BioMed Central Ltd. 2009
- Published: 13 July 2009
- Reinforcement Learning
- Sensory Input
- Motor Command
- Grayscale Image
- Time Structure
Animals are confronted with the problem of initiating motor actions based on very complex sensory input. We have built a biologically plausible model that uses reinforcement learning on complex visual stimuli to direct an agent towards a target. This is made possible by first extracting a high-level representation of the scene with a hierarchical network and then applying a correlation based RL-learning rule.
- Franzius M, Wilbert N, Wiskott L: Invariant object recognition with slow feature analysis. Proc 18th Int'l Conf on Artificial Neural Networks. Edited by: Kurková V, Neruda R, Koutník J. 2008, Springer-Verlag, 961-970.Google Scholar
- Wiskott L, Sejnowski TJ: Slow feature analysis: Unsupervised learning of invariances. Neural Computation. 2002, 14: 715-770. 10.1162/089976602317318938.PubMedView ArticleGoogle Scholar
- Zito T, Wilbert N, Wiskott L, Berkes P: Modular toolkit for data processing (MDP): A Python data processing framework. Front Neuroinformatics. 2008, 2: 8.PubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd.