Reinforcement learning (RL) has shown to reach super human-level performance across a wide range of tasks. However, unlike supervised machine learning, learning strategies that generalize well to a wide range of situations remains one of the most challenging problems for real-world RL. Autonomous driving (AD) provides a multi-faceted experimental field, as it is necessary to learn the correct behavior over many variations of road layouts and large distributions of possible traffic situations, including individual driver personalities and hard-to-predict traffic events. In this paper we propose a challenging benchmark for generalizable RL for AD based on a configurable, flexible, and performant code base. Our benchmark uses a catalog of randomized scenario generators, including multiple mechanisms for road layout and traffic variations, different numerical and visual observation types, distinct action spaces, diverse vehicle models, and allows for use under static scenario definitions. In addition to purely algorithmic insights, our application-oriented benchmark also enables a better understanding of the impact of design decisions such as action and observation space on the generalizability of policies. Our benchmark aims to encourage researchers to propose solutions that are able to successfully generalize across scenarios, a task in which current RL methods fail. The code for the benchmark is available at https://github.com/seawee1/driver-dojo.

本论文基于可配置、灵活、高性能的代码库，提出了一种挑战性的自动驾驶通用强化学习基准测试方法，该方法使用随机化场景生成器的目录，包括多种不同的道路布局和交通变化机制、不同的数值和视觉观察类型、不同的动作空间、多种车辆模型等。该基准测试旨在鼓励研究人员提出能够成功地推广到各种场景的解决方案，目前的 RL 方法在这项任务中失败。

Driver Dojo: 自主驾驶通用强化学习基准测试