论文标题
使用软批评进行低级无人机控制
Using Soft Actor-Critic for Low-Level UAV Control
论文作者
论文摘要
从器官交付到远程位置再到无线网络覆盖的几个民用应用域中，无人驾驶汽车（无人机）或无人机最近被使用。但是，这些平台自然是不稳定的系统，已经提出了许多不同的控制方法。通常基于经典和现代控制，这些算法需要了解机器人的动态。但是，最近，无需任何知识机器人模型就成功地使用了无模型的增强学习来控制无人机。在这项工作中，我们提出了一个框架，以训练软批评者（SAC）算法，以在最佳目标任务中对四摩托车的低水平控制。所有实验均在模拟下进行。通过实验，我们表明SAC不仅可以学习强大的政策，而且还可以应对看不见的场景。来自仿真的视频可在https://www.youtube.com/watch?v=9z8vgs0ri5g和https://github.com/larocs/larocs/sac_uav中获得。
Unmanned Aerial Vehicles (UAVs), or drones, have recently been used in several civil application domains from organ delivery to remote locations to wireless network coverage. These platforms, however, are naturally unstable systems for which many different control approaches have been proposed. Generally based on classic and modern control, these algorithms require knowledge of the robot's dynamics. However, recently, model-free reinforcement learning has been successfully used for controlling drones without any prior knowledge of the robot model. In this work, we present a framework to train the Soft Actor-Critic (SAC) algorithm to low-level control of a quadrotor in a go-to-target task. All experiments were conducted under simulation. With the experiments, we show that SAC can not only learn a robust policy, but it can also cope with unseen scenarios. Videos from the simulations are available in https://www.youtube.com/watch?v=9z8vGs0Ri5g and the code in https://github.com/larocs/SAC_uav.
