The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning

An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm caused the organism’s behavior to evolve in response to selection pressure applied by reinforcement from the environment. The resulting behavior was consistent with the well-established quantitative law of effect, which asserts that the time rate of a behavior is a hyperbolic function of the time rate of reinforcement obtained for the behavior. The high-order, steady-state, hyperbolic relationship between behavior and reinforcement exhibited by the artificial organism did not depend on specific qualitative or quantitative features of the evolutionary algorithm, and it described the organism’s behavior significantly better than other, similar, function forms. This evolutionary algorithm is a good candidate for a dynamics of live behavior, and it might be a useful building block for more complex artificial organisms.