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Open Radio Access Network (O-RAN) has introduced an emerging RAN architecturethat enables openness, intelligence, and automated control. The RAN IntelligentController (RIC) provides the platform to design and deploy RAN controllers.xApps are the applications which will take this responsibility by leveragingmachine learning (ML) algorithms and acting in near-real time. Despite theopportunities provided by this new architecture, the progress of practicalartificial intelligence (AI)-based solutions for network control and automationhas been slow. This is mostly because of the lack of an endto-end solution fordesigning, deploying, and testing AI-based xApps fully executable in real O-RANnetwork. In this paper we introduce an end-to-end O-RAN design and evaluationprocedure and provide a detailed discussion of developing a ReinforcementLearning (RL) based xApp by using two different RL approaches and consideringthe latest released O-RAN architecture and interfaces.

Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis