Drive-reinforcement learning: a self-supervised model for adaptive control

Abstract

A network of two self-supervised simulated neurons using the drive-reinforcement rule for synaptic modification can learn to balance a pole without experiencing failure. This adaptive controller also responds quickly and automatically to rapidly changing plant parameters. Other aspects of the controller's performance investigated include the controller's response in a noisy environment, the effect of varying the partitioning of the state space of the plant, the effect of increasing the controller's response time, and the consequences of disabling learning at the beginning of a trial and during the progress of a trial. Earlier work with drive-reinforcement learning supports the claim that the theory's neuronal model can account for observed phenomena of classical conditioning; this work constitutes progress toward demonstrating that useful adaptive controllers can be fabricated from networks of classically conditionable elements.