  Over the years, reinforcement learning has emerged as a popular approach to
develop signal control and vehicle platooning strategies either independently
or in a hierarchical way. However, jointly controlling both in real-time to
alleviate traffic congestion presents new challenges, such as the inherent
physical and behavioral heterogeneity between signal control and platooning, as
well as coordination between them. This paper proposes an innovative solution
to tackle these challenges based on heterogeneous graph multi-agent
reinforcement learning and traffic theories. Our approach involves: 1)
designing platoon and signal control as distinct reinforcement learning agents
with their own set of observations, actions, and reward functions to optimize
traffic flow; 2) designing coordination by incorporating graph neural networks
within multi-agent reinforcement learning to facilitate seamless information
exchange among agents on a regional scale. We evaluate our approach through
SUMO simulation, which shows a convergent result in terms of various
transportation metrics and better performance over sole signal or platooning
control.
