Learning of perceptual states in the design of an adaptive wall-following behavior

In this work we propose a new model that aims to overcome some of the limitations that are associated with reinforcement learning. In order to do so, we include not only prior knowledge of the task to be undertaken, by means of the so-called Supervised Reinforcement Learning (SRL), but also the creation and adaptation of the perceptual states of the environment during the learning process itself, by means of the MART neural network.We have tested the application of this model on a wall following behaviour. The results obtained con rm the great usefulness and advantages that are derived from its employment.