# Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning

arXiv: https://arxiv.org/abs/2203.04700 · HTML: https://mlsanity.com/paper/2203.04700/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.10082/) (2017)
- [Deep Reinforcement Learning for Swarm Systems](https://mlsanity.com/paper/1807.06613/) (2018)
- [Decentralized Multi-Agent Pursuit using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.08193/) (2020)
- [Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader](https://mlsanity.com/paper/2001.04731/) (2020)

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

- [Learning Vision-based Pursuit-Evasion Robot Policies](https://mlsanity.com/paper/2308.16185/) (2023)
- [Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.15866/) (2024)
- [Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC](https://mlsanity.com/paper/2412.19669/) (2024)
- [A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments](https://mlsanity.com/paper/2312.12255/) (2023)
- [HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit](https://mlsanity.com/paper/2409.08767/) (2024)
- [TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learning](https://mlsanity.com/paper/2503.12395/) (2025)
- [ARBoids: Adaptive Residual Reinforcement Learning With Boids Model for Cooperative Multi-USV Target Defense](https://mlsanity.com/paper/2502.18549/) (2025)
- [Decentralized End-to-End Multi-AAV Pursuit Using Predictive Spatio-Temporal Observation via Deep Reinforcement Learning](https://mlsanity.com/paper/2603.24238/) (2026)
- [M2GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit](https://mlsanity.com/paper/2604.19404/) (2026)

## Abstract (arXiv)

It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.