Handover algorithm for space-air-ground integrated network based on location prediction model by XIE Jianli, CHEN Long, ZHANG Zepeng, LI Cuiran

“…Compared with network handover algorithms based on Q-Learning, double deep Q-network (DDQN), and dueling double deep Q-network (D3QN), the proposed algorithm performs better in terms of reducing handover delay and frequency, as well as enhancing network throughput, thereby validating the effectiveness of the proposed algorithm.…”
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Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method by Heng Xu, Dayong Zhu

“…By comparing the PODQN algorithm with random strategy, DQN, Double DQN, Dueling DQN, VDN, QMIX, it is demonstrated that the proposed PODQN algorithm has the best performance under different agent configurations.…”
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UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design by Jin Dai, Yunfei Gao, Chao Cai, Wei Xiong, Manjia Liu

“…Then we propose an efficient two-step optimization method that involves optimizing the UAV trajectory with a modified multi-step dueling DDQN (MSD-DDQN) algorithm and planning the mobile nest’s path using the A* algorithm. …”
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Tactical intent-driven autonomous air combat behavior generation method by Xingyu Wang, Zhen Yang, Shiyuan Chai, Jichuan Huang, Yupeng He, Deyun Zhou

“…Finally, to address the problems of poor stability and slow convergence speed of deep reinforcement learning algorithms in large-scale state-action spaces, the dueling-noisy-multi-step DQN algorithm is devised, which not only improves the accuracy of value function approximation but also enhances the efficiency of space exploration and network generalization. …”
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Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework by Haijiang Li, Jiapeng Zhao, Peng Jia, Hongdong Ou, Weili Zhao

“…Secondly, this study proposes an advanced solution methodology that employs a reinforcement learning technique integrating a Dueling Deep Q-Network and Double Deep Q-Network. …”
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