The RBF neural network in approximate dynamic programming

A radial basis function (RBF) neural network was applied to an optimal control problem. The role of an approximation architecture in the task of dynamic programming is emphasised. While it has been proved that dynamic programming works well for moderate discrete spaces, research is continuing on how to apply dynamic programming techniques to large discrete and continuous spaces. For continuous spaces there does not yet exist a universal approach, but it seems that a RBF network is able to solve the problem with a negligible amount of manual experimentation.