  The open radio access network (O-RAN) architecture supports intelligent
network control algorithms as one of its core capabilities. Data-driven
applications incorporate such algorithms to optimize radio access network (RAN)
functions via RAN intelligent controllers (RICs). Deep reinforcement learning
(DRL) algorithms are among the main approaches adopted in the O-RAN literature
to solve dynamic radio resource management problems. However, despite the
benefits introduced by the O-RAN RICs, the practical adoption of DRL algorithms
in real network deployments falls behind. This is primarily due to the slow
convergence and unstable performance exhibited by DRL agents upon deployment
and when encountering previously unseen network conditions. In this paper, we
address these challenges by proposing transfer learning (TL) as a core
component of the training and deployment workflows for the DRL-based
closed-loop control of O-RAN functionalities. To this end, we propose and
design a hybrid TL-aided approach that leverages the advantages of both policy
reuse and distillation TL methods to provide safe and accelerated convergence
in DRL-based O-RAN slicing. We conduct a thorough experiment that accommodates
multiple services, including real VR gaming traffic to reflect practical
scenarios of O-RAN slicing. We also propose and implement policy reuse and
distillation-aided DRL and non-TL-aided DRL as three separate baselines. The
proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the
average initial reward value and the percentage of converged scenarios, and a
64.6% decrease in reward variance while maintaining fast convergence and
enhancing the generalizability compared with the baselines.
