In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

翻译：本文深入探讨了截至2023年历史上最大规模旨在平顺交通流的自动驾驶车辆现场测试中所部署的强化学习算法的技术细节，揭示了为自动驾驶车辆开发强化学习控制器过程中遇到的挑战与突破。我们系统阐述了强化学习算法的基本概念及其在自动驾驶场景中的应用，详细讨论了从仿真到实际部署的全流程开发过程，涵盖仿真器设计及奖励函数构建等关键环节。通过仿真测试与实地部署的双重验证，展示了强化学习控制器在平顺交通流方面的实际效益。本文从马尔可夫决策过程的基础理论出发，逐步深入到深度强化学习等前沿技术，全面剖析驱动这一快速演进领域的理论基础与实践应用。我们同时展示了实际案例与替代性研究项目，突出强化学习控制器在革新自动驾驶技术中的关键作用——无论是应对复杂城市环境还是处理不可预测的交通场景，这些智能控制器都在不断突破自动驾驶车辆的能力边界。此外，本文着重探讨了将强化学习控制器部署至自动驾驶车辆所涉及的安全考量与硬件技术细节。由于此类算法通过与环境交互进行学习进化，确保其行为符合安全标准成为关键问题。我们系统梳理了当前为应对这些挑战而开发的方法论与框架，强调为自动驾驶车辆构建可靠控制系统的重要性。