… routing for wireless multihop networks: a deep reinforcement learning approach[J]. Journal
… Energy consumption is also a crucial issue in the study of a wireless multihop network to …

… early machine learning classifiers to the more modern deep learning methods that gained
popularity in recent … Our study uses Google’s open source language model, BERT, with some …

… machine learning and control systems. Recent development of (single-agent) deep
reinforcement … In this paper, we review recent advances on a sub-area of this topic: decentralized …

… all the required scenarios and needs further study. A “soft” Tanner graph … Deep learning for
intelligent wireless networks: a … A deepreinforcement learning approach for software-defined …

… based on smart textiles combined with machine learning. The goal of the review is to
understand and broaden the application of machine learning in the field of flexible strain sensors. …

… algorithms, we use a deep reinforcement learning (DRL) algorithm to solve real-time
decision-making problems. Further, we propose the dueling double deep Q-network (Dueling-…

… In response, we propose a novel multi-agent reinforcement learning based framework to
determine the optimal 3D trajectory of each UAV in a distributed manner without a central …

… ,提出了基于强化学习(RL, reinforcement learning)的空天地一体化网络… Then, the reinforcement
learning (RL) framework was … As a case study, the method of applying deep RL (DRL) was …

… A comparative simulation study is … Cellular Networks reviews relevant concepts and
definitions for performance evaluation of channel borrowing or user association schemes in cellular …

… Machine learning technology also has great application potential in loss assessment for
agrometeorological disasters. From the aspect of parameter prediction, the hydrological, soil …