AI RESEARCH
Multi-Agent DRL for V2X Resource Allocation: Disentangling Challenges and Benchmarking Solutions
arXiv CS.LG
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ArXi:2603.06607v1 Announce Type: cross Multi-agent deep reinforcement learning (DRL) has emerged as a promising approach for radio resource allocation (RRA) in cellular vehicle-to-everything (C-V2X) networks. However, the multifaceted challenges inherent to multi-agent reinforcement learning (MARL) - including non-stationarity, coordination difficulty, large action spaces, partial observability, and limited robustness and generalization - are often intertwined, making it difficult to understand their individual impact on performance in vehicular environments.