REINFORCEMENT LEARNING AND RECRUITMENT MECHANISM FOR ADAPTIVE DISTRIBUTED CONTROL

Abstract

The work presented in this paper is an attempt to spread further the inspiration gained from the knowledge of biological systems into the field of adaptive control. After the neural controllers and the evolutionary based mechanisms, new hints for the control of complex processes might be derived from other biological domains such as immunology or the study of conditioning learning. The conception of a system equipped with a complex controller, interacting with an uncertain and varying environment, and basing its learning on its own experiences entails quite naturally the integration of a reinforcement learning mechanism. Two learning processes characterized by two different time scales will be introduced, will be connected to their respective biological origins and will be illustrated on the classical cart-pole control problem. These two learning processes are the rapid reinforcement learning and the slower recruitment mechanism.