  Setting the transmit power setting of 5G cells has been a long-term topic of
discussion, as optimized power settings can help reduce interference and
improve the quality of service to users. Recently, machine learning (ML)-based,
especially reinforcement learning (RL)-based control methods have received much
attention. However, there is little discussion about the generalisation ability
of the trained RL models. This paper points out that an RL agent trained in a
specific indoor environment is room-dependent, and cannot directly serve new
heterogeneous environments. Therefore, in the context of Open Radio Access
Network (O-RAN), this paper proposes a distributed cell power-control scheme
based on Federated Reinforcement Learning (FRL). Models in different indoor
environments are aggregated to the global model during the training process,
and then the central server broadcasts the updated model back to each client.
The model will also be used as the base model for adaptive training in the new
environment. The simulation results show that the FRL model has similar
performance to a single RL agent, and both are better than the random power
allocation method and exhaustive search method. The results of the
generalisation test show that using the FRL model as the base model improves
the convergence speed of the model in the new environment.
