# Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones

arXiv: https://arxiv.org/abs/2304.03443 · HTML: https://mlsanity.com/paper/2304.03443/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Deep Reinforcement Learning from Self-Play in Imperfect-Information Games](https://mlsanity.com/paper/1603.01121/) (2016)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight](https://mlsanity.com/paper/1907.01531/) (2019)
- [RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight](https://mlsanity.com/paper/2007.03465/) (2020)
- [Decentralized Multi-Agent Pursuit using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.08193/) (2020)
- [Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing](https://mlsanity.com/paper/1810.06224/) (2018)
- [EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments](https://mlsanity.com/paper/2011.04183/) (2020)
- [Flying Through a Narrow Gap Using End-to-end Deep Reinforcement Learning Augmented with Curriculum Learning and Sim2Real](https://mlsanity.com/paper/2108.12869/) (2021)
- [Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach](https://mlsanity.com/paper/2204.12181/) (2022)
- [Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader](https://mlsanity.com/paper/2001.04731/) (2020)
- [NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images](https://mlsanity.com/paper/1807.08241/) (2018)
- [EVA-Planner: Environmental Adaptive Quadrotor Planning](https://mlsanity.com/paper/2011.04246/) (2020)

## Built on by (most cited first, 9 of 9)

- [Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding](https://mlsanity.com/paper/2506.10756/) (2025)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning](https://mlsanity.com/paper/2308.00331/) (2023)
- [FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation](https://mlsanity.com/paper/2505.20783/) (2025)
- [Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games](https://mlsanity.com/paper/2506.02849/) (2025)
- [Clustering-based Learning for UAV Tracking and Pose Estimation](https://mlsanity.com/paper/2405.16867/) (2024)
- [Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies](https://mlsanity.com/paper/2510.05692/) (2025)
- [Agile Interception of a Flying Target using Competitive Reinforcement Learning](https://mlsanity.com/paper/2603.16279/) (2026)
- [Reinforcement learning in pursuit-evasion differential game: safety, stability and robustness](https://mlsanity.com/paper/2507.19516/) (2025)

## Abstract (arXiv)

Safe navigation of drones in the presence of adversarial physical attacks from multiple pursuers is a challenging task. This paper proposes a novel approach, asynchronous multi-stage deep reinforcement learning (AMS-DRL), to train adversarial neural networks that can learn from the actions of multiple evolved pursuers and adapt quickly to their behavior, enabling the drone to avoid attacks and reach its target. Specifically, AMS-DRL evolves adversarial agents in a pursuit-evasion game where the pursuers and the evader are asynchronously trained in a bipartite graph way during multiple stages. Our approach guarantees convergence by ensuring Nash equilibrium among agents from the game-theory analysis. We evaluate our method in extensive simulations and show that it outperforms baselines with higher navigation success rates. We also analyze how parameters such as the relative maximum speed affect navigation performance. Furthermore, we have conducted physical experiments and validated the effectiveness of the trained policies in real-time flights. A success rate heatmap is introduced to elucidate how spatial geometry influences navigation outcomes. Project website: https://github.com/NTU-ICG/AMS-DRL-for-Pursuit-Evasion.