  We propose a general framework for policy representation for reinforcement
learning tasks. This framework involves finding a low-dimensional embedding of
the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS
based methods allows us to derive strong theoretical guarantees on the expected
return of the reconstructed policy. Such guarantees are typically lacking in
black-box models, but are very desirable in tasks requiring stability. We
conduct several experiments on classic RL domains. The results confirm that the
policies can be robustly embedded in a low-dimensional space while the embedded
policy incurs almost no decrease in return.
