ICANN '95, Conférence Internationale sur les Réseaux de Neurones Artificiels ; actes ; conference scientifique Herausgeber: Fogelman-Soulié, F. Erscheinungsort: Paris Verlag: EC2 & Cie Erscheinungsjahr: 1995 Seiten: 129-134 ISBN: 2-910085-18-X
Erstveröffentlichung
1995
Abstract (EN)
Q-learning as well as other learning paradigms depend strongly on the representation of the underlying state space. As a special case of the hidden state problem we investigate the effect of a self-organizing discretization of the state space in a simple control problem. We apply the neural gas algorithm with adaptation of learning rate and neighborhood range to a simulated cart-pole problem. The learning parameters are determined by the ambiguity of successful actions inside each cell.