  We consider the problem of dynamic platoon leader selection, user
association, channel assignment, and power allocation on a cellular
vehicle-to-everything (C-V2X) based highway, where multiple vehicle-to-vehicle
(V2V) and vehicle-to-infrastructure (V2I) links share the frequency resources.
There are multiple roadside units (RSUs) on a highway, and vehicles can form
platoons, which has been identified as an advanced use case to increase road
efficiency. The traditional optimization methods, requiring global channel
information at a central controller, are not viable for high-mobility vehicular
networks. To deal with this challenge, we propose a distributed multi-agent
reinforcement learning (MARL) for resource allocation (RA). Each platoon
leader, acting as an agent, can collaborate with other agents for joint
sub-band selection and power allocation for its V2V links, and joint user
association and power control for its V2I links. Moreover, each platoon can
dynamically select the vehicle most suitable to be the platoon leader. We aim
to maximize the V2V and V2I packet delivery probability in the desired latency
using the deep Q-learning algorithm. Simulation results indicate that our
proposed MARL outperforms the centralized hill-climbing algorithm, and platoon
leader selection helps to improve both V2V and V2I performance.
