Reinforcement learning and automatic categorization

Abstract

The categorization process defines sensor and action categories from elementary sensor readings and basic actions so that the necessary elements for solving a task are correctly perceived and manipulated. In reinforcement learning, a previous categorization process is needed to define sensor and action categories with special requirements that we analyze and that, in general, are difficult to achieve, especially in complex tasks such as those that arise when working with autonomous robots. We show how these special requirements should be relaxed and we sketch a reinforcement learning algorithm that uses a less restrictive form of sensory categorization than existing algorithms. Additionally, we show how a given sensory categorization can be improved so that it better fits the demands of the previous algorithm.