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
Published in:
Electronics Letters
(Volume:34
,
Issue:
21
)
Date of Publication: 15 Oct 1998