  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.
