论文标题
从模拟到现实世界的操纵执行
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning
论文作者
论文摘要
事实证明，深厚的强化学习能够在不同领域解决许多控制任务，但是在现实世界中部署时，这些系统的行为并不总是像预期的那样。这主要是由于模拟数据和现实世界数据之间缺乏域的适应性以及火车和测试数据集之间的区别。在这项工作中，我们在自主驾驶领域中调查了这些问题，尤其是对于环绕式插入的机动规划模块。特别是，我们提出了一个基于多种环境的系统，在多种环境中，同时训练了代理，在不同情况下评估了模型的行为。最后，我们分析了旨在减少模拟数据和现实世界数据之间差距的技术，表明这增加了在看不见和现实世界情景下系统的概括能力。
Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real-world scenarios. This is mainly due to the lack of domain adaptation between simulated and real-world data together with the absence of distinction between train and test datasets. In this work, we investigate these problems in the autonomous driving field, especially for a maneuver planning module for roundabout insertions. In particular, we present a system based on multiple environments in which agents are trained simultaneously, evaluating the behavior of the model in different scenarios. Finally, we analyze techniques aimed at reducing the gap between simulated and real-world data showing that this increased the generalization capabilities of the system both on unseen and real-world scenarios.
