TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning
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 ...
| Authors | Nan Zhang, Zishuo Wang, Shuyu Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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