Digital twin (DT) platforms are increasingly regarded as a promising
technology for controlling, optimizing, and monitoring complex engineering
systems such as next-generation wireless networks. An important challenge in
adopting DT solutions is their reliance on data collected offline, lacking
direct access to the physical environment. This limitation is particularly
severe in multi-agent systems, for which conventional multi-agent reinforcement
(MARL) requires online interactions with the environment. A direct application
of online MARL schemes to an offline setting would generally fail due to the
epistemic uncertainty entailed by the limited availability of data. In this
work, we propose an offline MARL scheme for DT-based wireless networks that
integrates distributional RL and conservative Q-learning to address the
environment's inherent aleatoric uncertainty and the epistemic uncertainty
arising from limited data. To further exploit the offline data, we adapt the
proposed scheme to the centralized training decentralized execution framework,
allowing joint training of the agents' policies. The proposed MARL scheme,
referred to as multi-agent conservative quantile regression (MA-CQR) addresses
general risk-sensitive design criteria and is applied to the trajectory
planning problem in drone networks, showcasing its advantages.

提出了一种适用于基于数字孪生的无线网络的离线多智能体保守分位回归 (MA-CQR) 方案，通过集成分布式强化学习和保守 Q 学习来解决环境的内在的随机性不确定性和数据有限性导致的认识不确定性。在无人机网络中应用该方案，展示了其对轨迹规划问题的优势。