The optimization of Precoding Matrix Indicators (PMIs) is crucial for enhancing the performance of 5G networks, particularly in dense deployments where inter-cell interference is a significant challenge. Some approaches have leveraged AI/ML techniques for beamforming and beam selection, however, these methods often overlook the multi-objective nature of PMI selection, which requires balancing spectral efficiency (SE) and interference reduction. This paper proposes an interference-aware PMI selection method using an Advantage Actor-Critic (A2C) reinforcement learning model, designed for deployment within an O-RAN framework as an xApp. The proposed model prioritizes user equipment (UE) based on a novel strategy and adjusts PMI values accordingly, with interference management and efficient resource utilization. Experimental results in an O-RAN environment demonstrate the approach's effectiveness in improving network performance metrics, including SE and interference mitigation.