Efficient Q-Learning by division of labour.

Authors: 
Herrmann, Michael
Der, Ralf
Year: 
1995
Language: 
English
Abstract: 
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.
Appeared / Erschienen in: 
Proc. International Conference on Artificial Neural Networks - ICANN'95, Vol. II, S.129-134, Paris 1995
Pubdate / Erscheinungsdatum: 
1995
Pages / Seitenanzahl: 
6
AttachmentSize
1995-13.pdf141.09 KB