论文标题
SIM到现实的强化学习应用于端到端车辆控制
Sim-to-real reinforcement learning applied to end-to-end vehicle control
论文作者
论文摘要
在这项工作中，我们研究了有关车辆控制问题的基于视觉的端到端强化学习，例如沿线和避免碰撞。我们的控制器政策能够控制一个小规模的机器人，以沿着真正的两车道道路的右手车道，而训练仅在模拟中进行。我们的模型是由简单的卷积网络实现的，仅依靠向前的单眼相机的图像，并生成直接控制车辆的连续动作。为了训练这项政策，我们使用了近端政策优化，并实现了实际绩效所需的概括能力，我们使用了域随机化。我们通过测量多个绩效指标并将其与依赖其他方法的基准进行比较，对训练有素的政策进行了彻底的分析。为了评估模拟到真实性转移学习过程的质量和控制器在现实世界中的性能，我们在真实轨道上测量了简单的指标，并将这些指标与匹配模拟的结果进行了比较。通过可视化显着对象图进行了进一步的分析。
In this work, we study vision-based end-to-end reinforcement learning on vehicle control problems, such as lane following and collision avoidance. Our controller policy is able to control a small-scale robot to follow the right-hand lane of a real two-lane road, while its training was solely carried out in a simulation. Our model, realized by a simple, convolutional network, only relies on images of a forward-facing monocular camera and generates continuous actions that directly control the vehicle. To train this policy we used Proximal Policy Optimization, and to achieve the generalization capability required for real performance we used domain randomization. We carried out thorough analysis of the trained policy, by measuring multiple performance metrics and comparing these to baselines that rely on other methods. To assess the quality of the simulation-to-reality transfer learning process and the performance of the controller in the real world, we measured simple metrics on a real track and compared these with results from a matching simulation. Further analysis was carried out by visualizing salient object maps.
