Combining Deep Reinforcement Learning And Local Control For The Acrobot Swing-up And Balance Task
This work provides an incremental improvement for control engineers working on robotic control problems, specifically for tasks like the acrobot swing-up.
This paper combines a deep reinforcement learning algorithm (soft actor critic) with traditional controllers to solve the acrobot swing-up and balance task. The proposed technique outperforms other state-of-the-art reinforcement learning algorithms in this specific setting.
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.