  The capacity sharing problem in Radio Access Network (RAN) slicing deals with
the distribution of the capacity available in each RAN node among various RAN
slices to satisfy their traffic demands and efficiently use the radio
resources. While several capacity sharing algorithmic solutions have been
proposed in the literature, their practical implementation still remains as a
gap. In this paper, the implementation of a Reinforcement Learning-based
capacity sharing algorithm over the O-RAN architecture is discussed, providing
insights into the operation of the involved interfaces and the containerization
of the solution. Moreover, the description of the testbed implemented to
validate the solution is included and some performance and validation results
are presented.
