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The Open Radio Access Network (Open RAN) paradigm, and its referencearchitecture proposed by the O-RAN Alliance, is paving the way toward open,interoperable, observable and truly intelligent cellular networks. Crucial tothis evolution is Machine Learning (ML), which will play a pivotal role byproviding the necessary tools to realize the vision of self-organizing O-RANsystems. However, to be actionable, ML algorithms need to demonstrate highreliability, effectiveness in delivering high performance, and the ability toadapt to varying network conditions, traffic demands and performancerequirements. To address these challenges, in this paper we propose a novelDeep Reinforcement Learning (DRL) agent design for O-RAN applications that canlearn control policies under varying Service Level Agreement (SLAs) withheterogeneous minimum performance requirements. We focus on the case of RANslicing and SLAs specifying maximum tolerable end-to-end latency levels. We usethe OpenRAN Gym open-source environment to train a DRL agent that can adapt tovarying SLAs and compare it against the state-of-the-art. We show that ouragent maintains a low SLA violation rate that is 8.3x and 14.4x lower thanapproaches based on Deep Q- Learning (DQN) and Q-Learning while consumingrespectively 0.3x and 0.6x fewer resources without the need for re-training.

DRL-based Latency-Aware Network Slicing in O-RAN with Time-Varying SLAs