Abstract
This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UAV swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UAVs obtain the ability to adapt to complex environments where contours may be non-viable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms.
Abstract (translated)
本文提出了一种多智能体强化学习（MARL）框架，用于无人机群的协作避碰问题，并通过领域知识驱动奖励来实现这一目标。该奖励机制基于图像处理领域的知识，在二维场地上近似轮廓线。通过将障碍物建模为场地上的最大值点，碰撞自然得以避免，因为轮廓线不会穿过峰值或相交。此外，这种方法生成的路径是平滑且节能的。 我们的框架能够支持大规模无人机群训练，因为它减少了智能体之间的相互作用，并消除了最先进的MARL方法中复杂的信用分配方案或状态共享机制的需求。另外，通过密集训练，无人机获得了在复杂环境中适应的能力，在这些环境中轮廓可能不可行或不存在。 我们进行了大量的实验来评估所提出框架相对于现有最佳MARL算法的性能。
URL
https://arxiv.org/abs/2507.10913
