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arXiv · 2601.16578

Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab

Julius Beerwerth·Jianye Xu·Simon Schäfer·Fynn Belderink·Bassam Alrifaee
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Abstract

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.

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Julius Beerwerth, Jianye Xu, Simon Schäfer, Fynn Belderink, Bassam Alrifaee. 2026-01-23. Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab. https://doi.org/10.1515/auto-2025-0057

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