It is well known that, in one form or another, the variational Principle of Least Action (PLA) governs Nature. Although traditionally referred to explain physical phenomena, PLA has also been used to account for biological phenomena and even natural selection. However, its value in studying psychological processes has not been fully explored. In this paper we present a computational model, value-gradient learning, based on Pontryagin’s Minimum Principle (a version of PLA used in optimal theory), that applies to both classical and operant conditioning.