T. D'Orazio, G. Cicirelli and G. Attolico, Istituto Elaborazione Segnali ed Immagini - C.N.R.; C. Distante, University of Massachusetts
Developing elementary behavior is the starting point for the realization of complex systems. In this paper we will describe a learning algorithm that realizes a simple goal-reaching behavior for an autonomous vehicle when no a-priori knowledge of the environment is provided. The state of the system is based on information provided by a visual sensor. A Q-learning algorithm associates the optimal action to each state, developing the optimal state-action rules. A few training trials are sufficient, in simulation, to learn the optimal policy since during the test trials the set of actions is initially limited. The state and the action sets are then enlarged, introducing fuzzy variables with their membership functions to the extent of tackling errors in state estimation due to the noise in the vision measurements. Experimental results, both in simulated and real environment, are shown.