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Reinforcement learning and recruitment mechanism for adaptive distributed control

โœ Scribed by H. Bersini


Publisher
Elsevier Science
Year
1992
Weight
765 KB
Volume
17
Category
Article
ISSN
0066-4138

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โœฆ Synopsis


AbstractThe work presented in thispaper is an attempt to spread further the inspiration gained from the knowledge of biological systems intothe field of adaptive control. After the neural controllers and theevolutionary based mechanisms, new hints for thecontrol of complex processes mightbe derived from otherbiological domains suchas immunology or the study of conditioning learning. The conception of a system equipped with a complex controller, interacting with an uncertain andvarying environment, andbasing its learning on its ownexperiences 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 arethe rapidreinforcement learning and theslower recruitment mechanism.


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