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

Combining Deep Reinforcement Learning And Local Control For The Acrobot Swing-up And Balance Task