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The next generation of networks will actively embrace artificial intelligence(AI) and machine learning (ML) technologies for automation networks and optimalnetwork operation strategies. The emerging network structure represented byOpen RAN (O-RAN) conforms to this trend, and the radio intelligent controller(RIC) at the centre of its specification serves as an ML applications host.Various ML models, especially Reinforcement Learning (RL) models, are regardedas the key to solving RAN-related multi-objective optimization problems.However, it should be recognized that most of the current RL successes areconfined to abstract and simplified simulation environments, which may notdirectly translate to high performance in complex real environments. One of themain reasons is the modelling gap between the simulation and the realenvironment, which could make the RL agent trained by simulation ill-equippedfor the real environment. This issue is termed as the sim2real gap. Thisarticle brings to the fore the sim2real challenge within the context of O-RAN.Specifically, it emphasizes the characteristics, and benefits that the digitaltwins (DT) could have as a place for model development and verification.Several use cases are presented to exemplify and demonstrate failure modes ofthe simulations trained RL model in real environments. The effectiveness of DTin assisting the development of RL algorithms is discussed. Then the currentstate of the art learning-based methods commonly used to overcome the sim2realchallenge are presented. Finally, the development and deployment concerns forthe RL applications realisation in O-RAN are discussed from the view of thepotential issues like data interaction, environment bottlenecks, and algorithmdesign.

Sim2real for Reinforcement Learning Driven Next Generation Networks