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:无线网络服务要求的动态和演变性质促使电信业考虑智能的自我调整强化学习代理商,以控制不断扩大的网络服务组合;预期随着未来6G网络的采用,将注入许多新型服务,有时这些服务将由网络外部应用来界定;为管理特定服务类型的需要而培训的RL代理商可能不理想,无法在没有领域适应的情况下管理不同的服务类型;我们为评估新服务和现有服务之间的距离提供了简单的杂交,并表明最原始服务的RL代理商通过明确界定的域适应过程迅速适应新的服务类型;我们的方法使经过培训的源政策能够适应动态变化的新情况,而无需重新制定新的政策,从而实现重要的计算和成本效益;这种领域适应技术可能很快为面对迅速变化的服务类型进行更加普遍的基于RL的服务管理奠定基础。</s>