  In this work we present a novel extension of soft actor critic, a state of
the art deep reinforcement algorithm. Our method allows us to combine
traditional controllers with learned neural network policies. This combination
allows us to leverage both our own domain knowledge and some of the advantages
of model free reinforcement learning. We demonstrate our algorithm by combining
a hand designed linear quadratic regulator with a learned controller for the
acrobot problem. We show that our technique outperforms other state of the art
reinforcement learning algorithms in this setting.
