Recently, there has been tremendous efforts by network operators and
equipment vendors to adopt intelligence and openness in the next generation
radio access network (RAN). The goal is to reach a RAN that can self-optimize
in a highly complex setting with multiple platforms, technologies and vendors
in a converged compute and connect architecture. In this paper, we propose two
nested actor-critic learning based techniques to optimize the placement of
resource allocation function, and as well, the decisions for resource
allocation. By this, we investigate the impact of observability on the
performance of the reinforcement learning based resource allocation. We show
that when a network function (NF) is dynamically relocated based on service
requirements, using reinforcement learning techniques, latency and throughput
gains are obtained.

本文提出两种基于 actor-critic 学习的技术来优化资源分配功能的位置以及资源分配决策，并研究了增强学习资源分配在可观测性方面对性能的影响，实验表明，在动态地网络功能（NF）根据服务要求进行重新定位时，使用强化学习技术能够获得延迟和吞吐量的提高。