# Autonomous Decision Making for UAV Cooperative Pursuit-Evasion Game with Reinforcement Learning

arXiv: https://arxiv.org/abs/2411.02983 · HTML: https://mlsanity.com/paper/2411.02983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1911.04094/) (2019)
- [Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration](https://mlsanity.com/paper/1911.12504/) (2019)
- [EA-Net: Edge-Aware Network for Flow-based Video Frame Interpolation](https://mlsanity.com/paper/2105.07673/) (2021)

## Built on by (most cited first, 1 of 1)

- [SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision](https://mlsanity.com/paper/2602.23719/) (2026)

## Abstract (arXiv)

The application of intelligent decision-making in unmanned aerial vehicle (UAV) is increasing, and with the development of UAV 1v1 pursuit-evasion game, multi-UAV cooperative game has emerged as a new challenge. This paper proposes a deep reinforcement learning-based model for decision-making in multi-role UAV cooperative pursuit-evasion game, to address the challenge of enabling UAV to autonomously make decisions in complex game environments. In order to enhance the training efficiency of the reinforcement learning algorithm in UAV pursuit-evasion game environment that has high-dimensional state-action space, this paper proposes multi-environment asynchronous double deep Q-network with priority experience replay algorithm to effectively train the UAV's game policy. Furthermore, aiming to improve cooperation ability and task completion efficiency, as well as minimize the cost of UAVs in the pursuit-evasion game, this paper focuses on the allocation of roles and targets within multi-UAV environment. The cooperative game decision model with varying numbers of UAVs are obtained by assigning diverse tasks and roles to the UAVs in different scenarios. The simulation results demonstrate that the proposed method enables autonomous decision-making of the UAVs in pursuit-evasion game scenarios and exhibits significant capabilities in cooperation.