This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand.

The algorithm consists of two networks, an Actor and a Critic network, which approximate the policy and value functions of a reinforcement learning problem. The name DDPG, or Deep Deterministic Policy ...

As cities continue to expand, railways are expected to become an important component of urban mobility systems. Compared with automobiles and air transport, railways are highly energy efficient and ...

Abstract: This paper focused on three application problems of the traditional Deep Deterministic Policy Gradient(DDPG) algorithm. That is, the agent exploration is insufficient, the neural network ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

With the advancement of technology and the continuous innovation of applications, low-latency applications such as drones, online games and virtual reality are gradually becoming popular demands in ...

The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a ...

To address the limitations of the traditional maglev control algorithm that relies on mathematical models and is difficult to deploy, a single-point maglev system controller with improved DDPG based ...