The coordination of multiple autonomous agents in high-speed, competitive environments represents a significant engineering challenge. This paper presents CRUISE (Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing), a reinforcement learning framework designed to solve this challenge in the demanding domain of multi-drone racing. CRUISE overcomes key scalability limitations by synergistically combining a progressive difficulty curriculum with an efficient self-play mechanism to foster robust competitive behaviors. Validated in high-fidelity simulation with realistic quadrotor dynamics, the resulting policies significantly outperform both a standard reinforcement learning baseline and a state-of-the-art game-theoretic planner. CRUISE achieves nearly double the planner's mean racing speed, maintains high success rates, and demonstrates robust scalability as agent density increases. Ablation studies confirm that the curriculum structure is the critical component for this performance leap. By providing a scalable and effective training methodology, CRUISE advances the development of autonomous systems for dynamic, competitive tasks and serves as a blueprint for future real-world deployment.

翻译：在高速竞争环境中协调多个自主智能体是一项重大的工程挑战。本文提出CRUISE（基于课程学习的迭代自博弈可扩展多无人机竞速），这是一个强化学习框架，旨在解决多无人机竞速这一严苛领域中的上述挑战。CRUISE通过将渐进难度课程与高效自博弈机制协同结合，以培养鲁棒的竞争行为，从而克服了关键的可扩展性限制。在具有真实四旋翼动力学的高保真仿真中验证，所得策略显著优于标准强化学习基线和最先进的博弈论规划器。CRUISE实现了近乎规划器两倍的平均竞速速度，保持了高成功率，并在智能体密度增加时展现出鲁棒的可扩展性。消融研究证实课程结构是实现此性能飞跃的关键组件。通过提供一种可扩展且有效的训练方法，CRUISE推动了面向动态竞争任务的自主系统发展，并为未来的现实世界部署提供了蓝图。