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We consider the distributed resource selection problem in Vehicle-to-vehicle(V2V) communication in the absence of a base station. Each vehicle autonomouslyselects transmission resources from a pool of shared resources to disseminateCooperative Awareness Messages (CAMs). This is a consensus problem where eachvehicle has to select a unique resource. The problem becomes more challengingwhen---due to mobility---the number of vehicles in vicinity of each other ischanging dynamically. In a congested scenario, allocation of unique resourcesfor each vehicle becomes infeasible and a congested resource allocationstrategy has to be developed. The standardized approach in 5G, namelysemi-persistent scheduling (SPS) suffers from effects caused by spatialdistribution of the vehicles. In our approach, we turn this into an advantage.We propose a novel DIstributed Resource Allocation mechanism using multi-agentreinforcement Learning (DIRAL) which builds on a unique state representation.One challenging issue is to cope with the non-stationarity introduced byconcurrently learning agents which causes convergence problems in multi-agentlearning systems. We aimed to tackle non-stationarity with unique staterepresentation. Specifically, we deploy view-based positional distribution as astate representation to tackle non-stationarity and perform complex jointbehavior in a distributed fashion. Our results showed that DIRAL improves PRRby 20% compared to SPS in challenging congested scenarios.

Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V Communication