Temporal differences learning with the scaled conjugate gradient algorithm

This paper investigates the use of the scaled conjugate gradient algorithm in temporal differences learning for time series prediction more than one time interval ahead. Although neural networks trained with the traditional backpropagation (BP) algorithm are successfully applied in this area, the temporal differences (TD) methodology is potentially more applicable for multi-step predictions. A combination of TD with advanced algorithms like the scaled conjugate gradient (SCG) algorithm may prove more promising, resulting to robust learning systems. Whether, though, TD is better than supervised learning when examined with a solid training algorithm like SCG is an open issue. The results of this study indicate that the SCG algorithm, which was developed for supervised learning, cannot be directly applied in TD(λ) learning.