An unmanned air combat system based on swarm intelligence

Zhou, Wenqing; Zhu, Jihong; Kuang, Minchi

doi:10.1360/ssi-2019-0196

Cited by 10 publications

(3 citation statements)

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“…In recent years, deep RL (DRL), which combines RL and deep learning (DL), has emerged as an important research area in UAV control and decision-making. DRL alleviates the dimension explosion problem that easily occurs in traditional RL and has made great breakthroughs in areas such as robot control [21][22][23], scheduling optimization [24,25], and multi-agent collaboration [26][27][28][29]. Ma [30] proposed a task-assignment algorithm based on Deep Q Network (DQN) to support UAV swarm operations, which significantly improves the success rate of UAV swarm combat.…”

Section: Related Workmentioning

confidence: 99%

Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

Liu

Wang

et al. 2023

Drones

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UAV swarm applications are critical for the future, and their mission-planning and decision-making capabilities have a direct impact on their performance. However, creating a dynamic and scalable assignment algorithm that can be applied to various groups and tasks is a significant challenge. To address this issue, we propose the Extensible Multi-Agent Deep Deterministic Policy Gradient (Ex-MADDPG) algorithm, which builds on the MADDPG framework. The Ex-MADDPG algorithm improves the robustness and scalability of the assignment algorithm by incorporating local communication, mean simulation observation, a synchronous parameter-training mechanism, and a scalable multiple-decision mechanism. Our approach has been validated for effectiveness and scalability through both simulation experiments in the Multi-Agent Particle Environment (MPE) and a real-world experiment. Overall, our results demonstrate that the Ex-MADDPG algorithm is effective in handling various groups and tasks and can scale well as the swarm size increases. Therefore, our algorithm holds great promise for mission planning and decision-making in UAV swarm applications.

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