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We introduce a new class of Reinforcement Learning algorithms designed to operate in perceptual spaces containing images. They work by classifying the percepts using a computer vision algorithm specialized in image recognition, hence reducing the visual percepts to a symbolic class. This approach has the advantage of overcoming to some extent the curse of dimensionality by focusing the attention of the agent on distinctive and robust visual features. The visual classes are learned automaticallydoi:10.1007/1-84628-102-4_21 dblp:conf/sgai/JodogneP04 fatcat:s5smqkh2sjbplbb5xlfdmh6xoe