Operant conditioning in skinnerbots
Adaptive Behavior - Special issue on environment structure and behavior
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Behavioural Choice: An Investigation into Herrnstein's Matching Law
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Modeling Group Foraging: Individual Suboptimality, Interference, and a Kind of Matching
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
IEEE Transactions on Neural Networks
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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.