Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

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.

Automated summary

UAV swarm applications are crucial for the future, but creating a dynamic and scalable assignment algorithm is a challenge. To address this, we propose the Ex-MADDPG algorithm, which improves robustness and scalability. Through simulations and real-world experiments, our results demonstrate the effectiveness and scalability of the Ex-MADDPG algorithm in handling various groups and tasks. This algorithm holds great promise for mission planning and decision-making in UAV swarm applications.