基于课程学习的迭代自博弈用于可扩展多无人机竞速
Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing
AI总结：
本文提出CRUISE框架，结合渐进课程与自博弈训练多无人机竞速策略，在高保真仿真中速度接近两倍于博弈论规划器，验证了课程结构的关键作用。
摘要：
在高速、竞争性环境中协调多个自主智能体是一项重大的工程挑战。本文提出了CRUISE（基于课程学习的迭代自博弈用于可扩展多无人机竞速），这是一种强化学习框架，旨在解决多无人机竞速这一高要求领域中的挑战。CRUISE通过将渐进难度课程与高效的自博弈机制协同结合，克服了关键的可扩展性限制，从而培养出鲁棒的竞争行为。在具有真实四旋翼动力学的高保真仿真中进行的验证表明，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实现了接近规划器两倍的平均竞速速度，保持了较高的成功率，并随着智能体密度的增加展现出鲁棒的可扩展性。消融研究证实，课程结构是实现这一性能飞跃的关键组成部分。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的发展，并为未来的实际部署提供了蓝图。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
