The dynamic and evolutionary nature of service requirements in wireless networks has motivated the telecom industry to consider intelligent self-adapting Reinforcement Learning (RL) agents for controlling the growing portfolio of network services. Infusion of many new types of services is anticipated with future adoption of 6G networks, and sometimes these services will be defined by applications that are external to the network. An RL agent trained for managing the needs of a specific service type may not be ideal for managing a different service type without domain adaptation. We provide a simple heuristic for evaluating a measure of proximity between a new service and existing services, and show that the RL agent of the most proximal service rapidly adapts to the new service type through a well defined process of domain adaptation. Our approach enables a trained source policy to adapt to new situations with changed dynamics without retraining a new policy, thereby achieving significant computing and cost-effectiveness. Such domain adaptation techniques may soon provide a foundation for more generalized RL-based service management under the face of rapidly evolving service types.

翻译：无线网络中服务需求的动态演进特性，促使电信行业考虑采用具备智能自适应的强化学习智能体来控制日益增长的网络服务组合。随着未来6G网络的部署，大量新型服务将不断涌现，其中部分服务由网络外部的应用程序定义。针对特定服务类型需求训练的强化学习智能体，在未经域自适应的情况下可能无法有效管理不同类型的服务。我们提出一种简单启发性方法，用于评估新型服务与现有服务之间的邻近度度量，并证明通过定义明确的域自适应过程，最邻近服务的强化学习智能体能快速适应新型服务类型。本方法使预训练的源策略能够在动态变化的新场景中完成自适应，无需重新训练新策略，从而显著提升计算效率与成本效益。此类域自适应技术将为面向快速演进服务类型的通用化强化学习服务管理奠定基础。