We present a reproducible benchmark for evaluating sim-to-real transfer of Multi-Agent Reinforcement Learning (MARL) policies for Connected and Automated Vehicles (CAVs). The platform, based on the Cyber-Physical Mobility Lab (CPM Lab) [1], integrates simulation, a high-fidelity digital twin, and a physical testbed, enabling structured zero-shot evaluation of MARL motion-planning policies. We demonstrate its use by deploying a SigmaRL-trained policy [2] across all three domains, revealing two complementary sources of performance degradation: architectural differences between simulation and hardware control stacks, and the sim-to-real gap induced by increasing environmental realism. The open-source setup enables systematic analysis of sim-to-real challenges in MARL under realistic, reproducible conditions.
@article{arxiv.2601.16578,
title = {Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab},
author = {Julius Beerwerth and Jianye Xu and Simon Schäfer and Fynn Belderink and Bassam Alrifaee},
journal= {arXiv preprint arXiv:2601.16578},
year = {2026}
}