  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.
