Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems
In this paper, we study a vehicle-to-infrastructure (V2I) system where distributed base stations (BSs) acting as road-side units (RSUs) collect multimodal (wireless and visual) data from moving vehicles. We consider a decentralized rate maximization problem, where each RSU relies on its local observations to optimize its resources, while all RSUs must collaborate to guarantee favorable network performance. We recast this problem as a distributed multi-agent reinforcement learning (MARL) problem, by incorporating rotation symmetries in terms of vehicles' locations. To exploit these symmetries, we propose a novel self-supervised learning framework where each BS agent aligns the latent featu...
| Authors | Charbel Bou Chaaya, Mehdi Bennis |
|---|---|
| Primary category | cs.LG |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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