  We consider the distributed resource selection problem in Vehicle-to-vehicle
(V2V) communication in the absence of a base station. Each vehicle autonomously
selects transmission resources from a pool of shared resources to disseminate
Cooperative Awareness Messages (CAMs). This is a consensus problem where each
vehicle has to select a unique resource. The problem becomes more challenging
when---due to mobility---the number of vehicles in vicinity of each other is
changing dynamically. In a congested scenario, allocation of unique resources
for each vehicle becomes infeasible and a congested resource allocation
strategy has to be developed. The standardized approach in 5G, namely
semi-persistent scheduling (SPS) suffers from effects caused by spatial
distribution of the vehicles. In our approach, we turn this into an advantage.
We propose a novel DIstributed Resource Allocation mechanism using multi-agent
reinforcement Learning (DIRAL) which builds on a unique state representation.
One challenging issue is to cope with the non-stationarity introduced by
concurrently learning agents which causes convergence problems in multi-agent
learning systems. We aimed to tackle non-stationarity with unique state
representation. Specifically, we deploy view-based positional distribution as a
state representation to tackle non-stationarity and perform complex joint
behavior in a distributed fashion. Our results showed that DIRAL improves PRR
by 20% compared to SPS in challenging congested scenarios.
