  Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture
that enables openness, intelligence, and automated control. The RAN Intelligent
Controller (RIC) provides the platform to design and deploy RAN controllers.
xApps are the applications which will take this responsibility by leveraging
machine learning (ML) algorithms and acting in near-real time. Despite the
opportunities provided by this new architecture, the progress of practical
artificial intelligence (AI)-based solutions for network control and automation
has been slow. This is mostly because of the lack of an endto-end solution for
designing, deploying, and testing AI-based xApps fully executable in real O-RAN
network. In this paper we introduce an end-to-end O-RAN design and evaluation
procedure and provide a detailed discussion of developing a Reinforcement
Learning (RL) based xApp by using two different RL approaches and considering
the latest released O-RAN architecture and interfaces.
