In this paper, we address the issue of automating network configurations for dynamic network environments such as the Internet of Vehicles (IoV). Configuring network settings in IoV environments has proven difficult due to their dynamic and self-organizing nature. To address this issue, we propose a deep reinforcement learning-based approach to configure IoV network settings automatically. Specifically, we use a collection of neural networks to convert the observations of a communication environment (channel power gain, cross-channel power gain, etc.) into key features, which are then supplied to a deep$Q$neural network (DQN) as input for training. Afterward, the DQN will select the optimal network configuration for vehicles in the IoV environment. In addition, our approach considers both centralized and distributed training strategies. The centralized training strategy conducts the DQN training process on a roadside server, while the distributed training strategy trains the DQN on vehicles locally. Through our designed IoV simulation platform, we evaluate the efficacy of our proposed approach, demonstrating that it can improve the quality of services (QoS) in the IoV environments concerning reliability, latency, and service satisfaction.

The paper presents the attributes of energy technologies that compared against one another, an approach that leads to overly simplistic caricatures from which improper conclusions are drawn about new technologies. The result for intermittent renewable energy resources such as wind and solar has been a public misperception that energy storage is needed for continued deployment of these technologies. This misperception persists despite continued expansion of renewable generation and systems-based technology analyses that conclude significant additional renewable energy deployments can be accommodated without additional energy storage. To understand the value of, and opportunities for, grid-scale storage, one must first understand the electricity grid.