Multi-Agent DRL for V2X Resource Allocation: Disentangling Challenges and Benchmarking Solutions

2026-03-10 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Advanced, extended

Summary

A study by Wang et al. systematically benchmarks multi-agent deep reinforcement learning (DRL) algorithms for radio resource allocation (RRA) in cellular vehicle-to-everything (C-V2X) networks. The researchers formulated C-V2X RRA as a series of multi-agent interference games with increasing complexity, each designed to isolate specific multi-agent reinforcement learning (MARL) challenges like non-stationarity, coordination difficulty, large action spaces, partial observability, and robustness/generalization. They developed large-scale training and testing datasets using SUMO-generated highway traces to capture diverse vehicular topologies and interference patterns. Through extensive benchmarking of eight representative MARL algorithms, the study identified policy robustness and generalization across diverse vehicular topologies as the most dominant challenge in C-V2X RRA. Notably, the best-performing actor-critic method outperformed the best value-based approach by 42% on the most challenging task, emphasizing the need for zero-shot policy transfer.

Key takeaway

For AI Scientists and Research Scientists developing solutions for C-V2X radio resource allocation, you should prioritize actor-critic DRL algorithms, particularly PPO-based methods, over value-based approaches. The critical focus must be on developing policies that exhibit strong robustness and generalization capabilities across a wide range of vehicular topologies, including unseen ones, to enable zero-shot transfer. Consider IPPO as a strong baseline for its balance of performance and scalability in these complex, dynamic environments.

Key insights

Policy robustness and generalization across diverse vehicular topologies are the critical challenges for C-V2X RRA.

Principles

• Actor-critic algorithms generally outperform value-based methods in complex MARL tasks.
• Centralized Training with Decentralized Execution (CTDE) benefits actor-critic methods in partial observability.
• Coordination difficulty is topology-dependent, not inherently severe in single-step environments.

Method

C-V2X RRA is formulated as a sequence of multi-agent interference games, progressively isolating MARL challenges. Algorithms are benchmarked using SUMO-generated vehicular topology datasets.

In practice

• Use IPPO as a baseline for C-V2X RRA due to its performance and scalability.
• Prioritize algorithms capable of zero-shot policy transfer across diverse topologies.
• Focus on efficient state representations beyond raw channel gains for generalization.

Topics

• Multi-Agent Reinforcement Learning
• C-V2X Communication
• Radio Resource Allocation
• Policy Generalization
• Actor-Critic Algorithms

Code references

• Deepinlab2023/V2X-MARL-Bench

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.