TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911574572138496 |
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| author | Zhang, Nan Wang, Zishuo Huang, Shuyu Diamantopoulos, Georgios Tziritas, Nikos Oikonomou, Panagiotis Theodoropoulos, Georgios |
| author_facet | Zhang, Nan Wang, Zishuo Huang, Shuyu Diamantopoulos, Georgios Tziritas, Nikos Oikonomou, Panagiotis Theodoropoulos, Georgios |
| contents | Decentralised online learning enables runtime adaptation in cyber-physical multi-agent systems, but when operating conditions change, learned policies often require substantial trial-and-error interaction before recovering performance. To address this, we propose TwinLoop, a simulation-in-the-loop digital twin framework for online multi-agent reinforcement learning. When a context shift occurs, the digital twin is triggered to reconstruct the current system state, initialise from the latest agent policies, and perform accelerated policy improvement with simulation what-if analysis before synchronising updated parameters back to the agents in the physical system. We evaluate TwinLoop in a vehicular edge computing task-offloading scenario with changing workload and infrastructure conditions. The results suggest that digital twins can improve post-shift adaptation efficiency and reduce reliance on costly online trial-and-error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06610 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning Zhang, Nan Wang, Zishuo Huang, Shuyu Diamantopoulos, Georgios Tziritas, Nikos Oikonomou, Panagiotis Theodoropoulos, Georgios Machine Learning Artificial Intelligence I.2.11; I.6.3 Decentralised online learning enables runtime adaptation in cyber-physical multi-agent systems, but when operating conditions change, learned policies often require substantial trial-and-error interaction before recovering performance. To address this, we propose TwinLoop, a simulation-in-the-loop digital twin framework for online multi-agent reinforcement learning. When a context shift occurs, the digital twin is triggered to reconstruct the current system state, initialise from the latest agent policies, and perform accelerated policy improvement with simulation what-if analysis before synchronising updated parameters back to the agents in the physical system. We evaluate TwinLoop in a vehicular edge computing task-offloading scenario with changing workload and infrastructure conditions. The results suggest that digital twins can improve post-shift adaptation efficiency and reduce reliance on costly online trial-and-error. |
| title | TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence I.2.11; I.6.3 |
| url | https://arxiv.org/abs/2604.06610 |