In the world of cloud services, the growing complexity of distributed applications and the increase in energy consumption necessitate more efficient management of resources. For this reason, orchestrators such as Kubernetes are widely employed to automate the handling of workloads and resource usage, determining moment by moment the most suitable node on which to start a new task. On the other hand, the expanding application of artificial intelligence algorithms, particularly reinforcement learning, opens up new development opportunities. These advancements allow the creation of increasingly autonomous and state-of-the-art systems. This thesis introduces and develops a different approach to scheduling within Kubernetes clusters.