Modelling transition dynamics in MDPs with RKHS embeddings

18 Jun 2012Steffen GrunewalderGuy LeverLuca BaldassarreMassi PontilArthur Gretton

We propose a new, nonparametric approach to learning and representing transition dynamics in Markov decision processes (MDPs), which can be combined easily with dynamic programming methods for policy optimisation and value estimation. This approach makes use of a recently developed representation of conditional distributions as \emph{embeddings} in a reproducing kernel Hilbert space (RKHS)... (read more)

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