面向多样化环境的多无人机追逃双课程学习框架
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments
AI总结：
本文提出双课程学习框架 DualCL，通过内在参数课程提议器和外部环境生成器从易到难训练多无人机追逃策略，在多样化环境中实现超过 90% 捕获率、缩短至少 27.5% 捕获时间步，并具备零样本迁移和仿真到真实环境的可迁移性。
摘要：
本文研究多无人机追逃问题，即一组无人机在带有障碍物的受限环境中协同捕获一个快速逃逸者。现有启发式算法对追逃问题进行了简化，通常缺乏富有表现力的协同策略，在逃逸者高速移动等极端场景下难以完成捕获。相比之下，强化学习（RL）已被应用于该问题，并有望获得高度协同的捕获策略。然而，基于强化学习的方法在训练复杂三维场景和多样化任务设置时面临巨大探索空间的挑战。无人机的动力学约束进一步限制了强化学习获取高性能捕获策略的能力。本文提出一种双课程学习框架 DualCL，用于解决多样化环境中的多无人机追逃问题，并展现出对未见场景的零样本迁移能力。DualCL 包含两个主要组件：内在参数课程提议器，其从易到难逐步提出内在参数以提升无人机的捕获能力；外部环境生成器，负责探索未解决场景并生成合适的外部环境参数训练分布。仿真实验结果表明，DualCL 显著优于基线方法，在训练场景中实现了超过 90% 的捕获率，并将捕获时间步至少缩短 27.5%。此外，DualCL 在未见环境中表现出最佳的零样本泛化能力。进一步地，我们验证了所提出的追捕策略从仿真到真实环境的可迁移性。更多细节可参见项目网站 https://sites.google.com/view/dualcl。
英文摘要：
This paper addresses multi-UAV pursuit-evasion, where a group of drones cooperates to capture a fast evader in a confined environment with obstacles. Existing heuristic algorithms, which simplify the pursuit-evasion problem, often lack expressive coordination strategies and struggle to capture the evader in extreme scenarios, such as when the evader moves at high speeds. In contrast, reinforcement learning (RL) has been applied to this problem and has the potential to obtain highly cooperative capture strategies. However, RL-based methods face challenges in training for complex 3-dimensional scenarios with diverse task settings due to the vast exploration space. The dynamics constraints of drones further restrict the ability of reinforcement learning to acquire high-performance capture strategies. In this work, we introduce a dual curriculum learning framework, named DualCL, which addresses multi-UAV pursuit-evasion in diverse environments and demonstrates zero-shot transfer ability to unseen scenarios. DualCL comprises two main components: the Intrinsic Parameter Curriculum Proposer, which progressively suggests intrinsic parameters from easy to hard to improve the capture capability of drones, and the External Environment Generator, tasked with exploring unresolved scenarios and generating appropriate training distributions of external environment parameters. The simulation experimental results show that DualCL significantly outperforms baseline methods, achieving over 90% capture rate and reducing the capture timestep by at least 27.5% in the training scenarios. Additionally, it exhibits the best zero-shot generalization ability in unseen environments. Moreover, we demonstrate the transferability of our pursuit strategy from simulation to real-world environments. Further details can be found on the project website at https://sites.google.com/view/dualcl.
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。
