As the industry of autonomous driving grows, so does the potential
interaction of groups of autonomous cars. Combined with the advancement of
Artificial Intelligence and simulation, such groups can be simulated, and
safety-critical models can be learned controlling the cars within. This study
applies reinforcement learning to the problem of multi-agent car parking, where
groups of cars aim to efficiently park themselves, while remaining safe and
rational. Utilising robust tools and machine learning frameworks, we design and
implement a flexible car parking environment in the form of a Markov decision
process with independent learners, exploiting multi-agent communication. We
implement a suite of tools to perform experiments at scale, obtaining models
parking up to 7 cars with over a 98.1% success rate, significantly beating
existing single-agent models. We also obtain several results relating to
competitive and collaborative behaviours exhibited by the cars in our
environment, with varying densities and levels of communication. Notably, we
discover a form of collaboration that cannot arise without competition, and a
'leaky' form of collaboration whereby agents collaborate without sufficient
state. Such work has numerous potential applications in the autonomous driving
and fleet management industries, and provides several useful techniques and
benchmarks for the application of reinforcement learning to multi-agent car
parking.

本文介绍了应用强化学习解决多智能体汽车停车问题的方法和结果，通过使用独立学习和多智能体通信的马尔可夫决策过程设计实现了灵活的停车环境，结果证明了这种方法的高效性和优越性，并发现了一种合作方式和合作中的漏洞，在自动驾驶和车队管理等领域具有潜在应用。