The selectively attentive environmental learning system

Abstract

The selectively attentive environmental learning system (SAELS), that is capable of formulating decision policies while operating under terminally applied, minimally descriptive, reinforcement feedback is discussed. This type of reinforcement signals only that the generated policy is correct, or incorrect, and provides no information on the closeness of the generated policy to the correct policy. SAELS uses the drive-reinforcement neuronal model that, through the predictive qualities of its learning, is capable of solving the temporal credit assignment problem that arises under these reinforcement conditions. It is shown that SAELS can generate the necessary decision policy to maneuver through a multi-intersection maze.<>