Offline and Distributional Reinforcement Learning for Radio Resource Management
This addresses the limitation of online RL in practical wireless networks where interaction is infeasible, though it appears incremental by combining existing offline and distributional RL techniques for a specific domain.
The paper tackled the problem of radio resource management in wireless networks by proposing an offline and distributional reinforcement learning scheme that enables training without online interaction and handles uncertainties. The result showed it outperforms conventional models and achieves a 10% gain over online RL.
Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to its reliance on online interaction with the environment, its role becomes limited in practical, real-world problems where online interaction is not feasible. In addition, traditional RL stands short in front of the uncertainties and risks in real-world stochastic environments. In this manner, we propose an offline and distributional RL scheme for the RRM problem, enabling offline training using a static dataset without any interaction with the environment and considering the sources of uncertainties using the distributions of the return. Simulation results demonstrate that the proposed scheme outperforms conventional resource management models. In addition, it is the only scheme that surpasses online RL with a 10 % gain over online RL.