  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.
