  Automating network processes without human intervention is crucial for the
complex 6G environment. This requires zero-touch management and orchestration,
the integration of Network Intelligence (NI) into the network architecture, and
the efficient lifecycle management of intelligent functions. Reinforcement
Learning (RL) plays a key role in this context, offering intelligent
decision-making capabilities suited to networks' dynamic nature. Despite its
potential, integrating RL poses challenges in model development and
application. To tackle those issues, we delve into designing, developing, and
validating RL algorithms for scaling network functions in service-based network
architectures such as Open Radio Access Network (O-RAN). It builds upon and
expands previous research on RL lifecycle management by proposing several RL
algorithms and Reward Functions (RFns). Our proposed methodology is anchored on
a dual approach: firstly, it evaluates the training performance of these
algorithms under varying RFns, and secondly, it validates their performance
after being trained to discern the practical applicability in real-world
settings. We show that, despite significant progress, the development stage of
RL techniques for networking applications, particularly in scaling scenarios,
still leaves room for significant improvements. This study underscores the
importance of ongoing research and development to enhance the practicality and
resilience of RL techniques in real-world networking environments.
