Adding reinforcement learning features to the neural-gas method
- 1 January 2000
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
- Vol. 4, 539-542 vol.4
- https://doi.org/10.1109/ijcnn.2000.860827
Abstract
We propose a new neural approach for approximating function using a reinforcement-type learning: each time the network generates an output, the environment responds with the scalar distance between the delivered output and the expected one. Thus, this distance is the only information the network can use to modify the estimation of the multi-dimensional output. This reinforcement feature is embedded in a neural-gas method, taking advantages of the different facilities it offers. We detail the global algorithm and we present some simulation results in order to show the behaviour of the developed method.Keywords
This publication has 2 references indexed in Scilit:
- 'Neural-gas' network for vector quantization and its application to time-series predictionIEEE Transactions on Neural Networks, 1993
- Implementation of self-organizing neural networks for visuo-motor control of an industrial robotIEEE Transactions on Neural Networks, 1993