In recent years, microservice-based architectures (MSA) have gathered con- siderable attention for their potential to revolutionize the design and deployment of large-scale applications. This model promotes flexible, loosely coupled, and finely engineered software, making them easier to manage and facilitating DevOps practices. A critical aspect of MSA is the efficient scaling of microservices while ef- fectively managing resource allocation in the midst of increasing load intensity. Scaling may be applied vertically, by adding more resources to individual mi- croservices, horizontally, by instantiating additional instances of congested mi- croservices, or both at the same time. Recent works have investigated machine learning techniques and, in particular, reinforcement learning (RL) [1] [2], [3] to enhance scaling mechanisms.