论文标题
具有不确定性的自动表面车辆的型号引用增强学习控制
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
论文作者
论文摘要
本文介绍了一种新型的模型 - 引用增强学习控制方法，用于不确定的自主表面车辆。拟议的控制将传统的控制方法与深度强化学习结合在一起。借助常规控制，我们可以确保基于学习的控制法为整体系统提供闭环稳定性，并有可能提高深钢筋学习的样本效率。通过加强学习，我们可以直接学习控制法律，以弥补建模不确定性。在拟议的控制中，使用常规控制方法来设计标称系统。名义系统还定义了不确定的自动驾驶汽车所需的性能。与传统的深入学习方法相比，我们提出的基于学习的控制可以提供稳定性保证和更好的样本效率。我们通过广泛的仿真结果证明了新算法的性能。
This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional control method with deep reinforcement learning. With the conventional control, we can ensure the learning-based control law provides closed-loop stability for the overall system, and potentially increase the sample efficiency of the deep reinforcement learning. With the reinforcement learning, we can directly learn a control law to compensate for modeling uncertainties. In the proposed control, a nominal system is employed for the design of a baseline control law using a conventional control approach. The nominal system also defines the desired performance for uncertain autonomous vehicles to follow. In comparison with traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm via extensive simulation results.
