论文标题
结合深度强化学习和当地控制，以进行杂技演员的摇摆和平衡任务
Combining Deep Reinforcement Learning And Local Control For The Acrobot Swing-up And Balance Task
论文作者
论文摘要
在这项工作中，我们展示了软演员评论家的新颖延伸，这是一种最先进的深入强化算法。我们的方法使我们能够将传统控制器与学习的神经网络政策相结合。这种组合使我们能够利用自己的领域知识和自由强化学习的一些优势。我们通过将手动设计的线性二次调节器与用于杂技员问题的学习控制器相结合来演示我们的算法。我们表明，在这种情况下，我们的技术优于其他最先进的学习算法。
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.
