Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques

In this paper, we propose an unsupervised neural network allowing a robot to learn sensory-motor associations with a delayed reward. The robot task is to learn the \meaning" of pictograms in order to \survive" in a maze. First, we introduce a new neural conditioning rule (PCR: Probabilistic Conditioning Rule) allowing to test hypotheses (associations between visual categories and movements) during a given time span. Afterwards, we describe a real maze experiment with our mobile robot. We propose a neural architecture overcoming the diiculty to build visual categories dynamically while associating them to movements. Third, we propose to use our algorithm on a simulation in order to test it exhaustively. We give the results for diierent kinds of mazes. Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasoning based on image recognition.