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- W2051244208 abstract "Abstract 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 probabilistic conditioning rule (PCR) allowing us to test hypotheses (associations between visual categories and movements) during a given time span. Second, we describe a real maze experiment with our mobile robot. We propose a neural architecture overcoming the difficulty 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 different kinds of mazes and we compare our system to an adapted version of the Q-learning algorithm. 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." @default.
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- W2051244208 date "1997-06-01" @default.
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- W2051244208 title "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques" @default.
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- W2051244208 doi "https://doi.org/10.1016/s0921-8890(97)80708-7" @default.
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