  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.
