… RL policies without costly online interactions, which is the strength of offline RL. In this paper,
we advocate adopting offline reinforcement learning [2] for wireless network optimization. …

… in network deployment. By this architecture, we make use of game theory and reinforcement
learning to fulfill the optimal … it offline learning method based on reinforcement learning. The …

… faced by wireless networks and overcome the shortages of traditional offline optimization, …
Despite the growing interest of applying RL in wireless networks, most existing works focus …

… of solving sophisticated network optimizations and self-… solutions to deadline-aware wireless
scheduling, compared to … More specifically, for the wireless network model with a given …

… 3.4, that this use of Optuna as an optimizer for RL agent and simulator environment
hyperparameters is entirely separate from our use of it as an offline mobile network optimizer. …

… and offline, and requires much fewer training data samples compared to RL since the training
data are all optimal … We review the basic concepts of supervised learning, reinforcement …

… Through the simulation results, we show that the optimal offline … video wireless transmissions
over heterogeneous networks. The … streaming in multi-node wireless networks within a time-…

… , as mentioned earlier offline algorithms are … optimization of wireless networked controlled
systems using model-free RL. The research emphasized the importance of wireless network …

… , next-generation wireless networks will witness an intelligent con… For both online and offline
DRL, the neural network is … and future directions for self-optimizing mobile HetNets. Thus, …

… The emerging 5G mobile networks are … apply reinforcement learning to derive the optimal
policy and to find the earlier-mentioned trade-off. For that, we will use different reinforcement …