# DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention

arXiv: https://arxiv.org/abs/2310.15699 · HTML: https://mlsanity.com/paper/2310.15699/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2023.

## Built on by (most cited first, 5 of 5)

- [CARE: Enhancing Safety of Visual Navigation through Collision Avoidance via Repulsive Estimation](https://mlsanity.com/paper/2506.03834/) (2025)
- [HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit](https://mlsanity.com/paper/2409.08767/) (2024)
- [Agile Interception of a Flying Target using Competitive Reinforcement Learning](https://mlsanity.com/paper/2603.16279/) (2026)
- [AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit](https://mlsanity.com/paper/2502.09762/) (2025)
- [Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales](https://mlsanity.com/paper/2607.04972/) (2026)

## Abstract (arXiv)

Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient cooperation. This paper proposes a cooperative pursuit algorithm named Decentralized Adaptive COOperative Pursuit via Attention (DACOOP-A) by empowering reinforcement learning with artificial potential field and attention mechanisms. An attention-based framework is developed to emphasize important neighbors by concurrently integrating the learned attention scores into observation embedding and inter-robot interaction rules. A KL divergence regularization is introduced to alleviate the resultant learning stability issue. Improvements in data efficiency and generalization are demonstrated through numerical simulations. Extensive quantitative analysis and ablation studies are performed to illustrate the advantages of the proposed modules. Real-world experiments are performed to justify the feasibility of deploying DACOOP-A in physical systems.