Towards Beyond 5G and 6G, Open Radio Access Network (Open-RAN) is a recent RAN architecture that promotes the decoupling of RAN components, virtualization, open interfaces, and the use of machine learning-based intelligent models. Operators can benefit from this architecture to optimize the network performance and reduce deployment and operation costs. Open-RAN paves the way for RAN-sharing, where multiple operators can share the same infrastructure instead of deploying their own. In this paper, we model the problem of allocating radio and computing resources to multiple operators with different services as an Integer Linear Programming (ILP) problem aiming to satisfy users' demands. Due to the high complexity of solving an ILP problem, we develop a policy-gradient-based Reinforcement Learning (RL) model that aims to dynamically allocate resources to operators. The simulation results demonstrate the higher efficiency of our RAN-sharing RL model as it improves the radio and CPU resource utilization compared to No-Sharing models that deploy double the amount of provisioned resources, as each operator has its own infrastructure, with its own base stations and computing resources. In the considered scenario, RL demonstrates up to 19.5% more RBs utilization and 27.4% more CPU utilization. This highlights the ability of the RL model to reduce operational and deployment costs. Additionally, the RL model outperforms static RAN-sharing algorithms thanks to its dynamic adaptation to operators' varying traffic. Precisely, it scores up to 12.3% and 17.6% more RBs and CPU utilization, respectively.