Model-free learning control for unstable system

The authors consider the problem of model-free control via a reinforcement learning method that uses, as additional learning information, the distance between sampled and represented states, embedded in actions that are the result of a distancewise local interpolation scheme over states. Fast learning and a reduced number of trials resulted, as demonstrated in the real-time control of a magnetic levitation system