  We consider the problem of joint channel assignment and power allocation in
underlaid cellular vehicular-to-everything (C-V2X) systems where multiple
vehicle-to-network (V2N) uplinks share the time-frequency resources with
multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and
autonomous vehicles to travel closely together. Due to the nature of high user
mobility in vehicular environment, traditional centralized optimization
approach relying on global channel information might not be viable in C-V2X
systems with large number of users. Utilizing a multi-agent reinforcement
learning (RL) approach, we propose a distributed resource allocation (RA)
algorithm to overcome this challenge. Specifically, we model the RA problem as
a multi-agent system. Based solely on the local channel information, each
platoon leader, acting as an agent, collectively interacts with each other and
accordingly selects the optimal combination of sub-band and power level to
transmit its signals. Toward this end, we utilize the double deep Q-learning
algorithm to jointly train the agents under the objectives of simultaneously
maximizing the sum-rate of V2N links and satisfying the packet delivery
probability of each V2V link in a desired latency limitation. Simulation
results show that our proposed RL-based algorithm provides a close performance
compared to that of the well-known exhaustive search algorithm.
