This paper introduces reconfiguration mechanisms for a 5th Generation (5G) Medium Access Control (MAC) scheduler, which uses Deep Reinforcement Learning (DRL) and is deployed as an xApp within the RAN Intelligent Controller (RIC) framework. The objective is to optimize the allocation of radio resources in 5G networks, seeking to meet Quality of Service (QoS) requirements while minimizing resource consumption. To this end, we propose the dynamic adjustment of configurable parameters in a Lyapunov-based scheduler, which addresses the challenges posed by the highly dynamic network environment, and the need to meet multiple QoS objectives. The DRL agent dynamically reconfigures the scheduling by leveraging Integrated Sensing and Communications (ISAC)-provided sensing data alongside conventional communication metrics. Exploiting the adaptability of DRL, our solution can effectively respond to fluctuating network conditions, thereby continuously enhancing scheduling decisions in real time. The proposal is evaluated through comprehensive simulations conducted over ns-3 5G-LENA, which demonstrate notable improvements in QoS performance and resource efficiency. In the analyzed scenarios, our proposed scheduling solution achieves a 30% reduction in radio resource utilization while maintaining full compliance with QoS requirements. These results highlight the great potential of exploiting DRL and sensing data to optimize MAC scheduling in modern wireless communication systems, offering a scalable and adaptive solution for 5G and future 6G networks.