Dynamic radio resource management (RRM) in wireless networks presents
significant challenges, particularly in the context of Radio Access Network
(RAN) slicing. This technology, crucial for catering to varying user
requirements, often grapples with complex optimization scenarios. Existing
Reinforcement Learning (RL) approaches, while achieving good performance in RAN
slicing, typically rely on online algorithms or behavior cloning. These methods
necessitate either continuous environmental interactions or access to
high-quality datasets, hindering their practical deployment. Towards addressing
these limitations, this paper introduces offline RL to solving the RAN slicing
problem, marking a significant shift towards more feasible and adaptive RRM
methods. We demonstrate how offline RL can effectively learn near-optimal
policies from sub-optimal datasets, a notable advancement over existing
practices. Our research highlights the inherent flexibility of offline RL,
showcasing its ability to adjust policy criteria without the need for
additional environmental interactions. Furthermore, we present empirical
evidence of the efficacy of offline RL in adapting to various service-level
requirements, illustrating its potential in diverse RAN slicing scenarios.

该研究介绍了离线强化学习（RL）在解决无线网络中的无线电资源管理（RRM）问题方面的应用，展示了它学习接近最优策略的能力，适应不同的服务级别要求，并在各种 RAN 分割场景中发挥潜力。