Associative Learning via Inhibitory Search

Abstract

ALVIS is a reinforcement-based connectionist architecture that learns associative maps in continuous multidimensional environments. The discovered locations of positive and negative reinforcements are recorded in do be and don't be subnetworks, respectively. The outputs of the subnetworks relevant to the current goal are combined and compared with the current location to produce an error vector. This vector is backpropagated through a motor-perceptual mapping network to produce an action vector that leads the system towards do-be locations and away from don't-be locations. ALVIS is demonstrated with a simulated robot posed a target-seeking task.