Multi-Agent Deep Reinforcement Learning for Cooperative UAV Swarm Navigation in Disaster Response
Keywords:
multi-agent deep reinforcement learning, UAV swarm, cooperative navigation, disaster response, system architecture, robustness, policy governanceAbstract
The increasing frequency and severity of natural disasters demand rapid, adaptive, and resilient response systems. Unmanned aerial vehicle swarms offer a promising platform for situational awareness, search and rescue, and logistics support in hazardous environments where human access is limited. However, effective coordination of large swarms under dynamic, partially observable, and communication-constrained conditions remains a formidable challenge. This paper presents a system-level examination of multi-agent deep reinforcement learning for cooperative UAV swarm navigation in disaster response. It examines architectural choices such as centralized training with decentralized execution, the structural trade-offs between global and local reward shaping, and the implications of communication infrastructure on learning efficiency and deployment robustness. The discussion extends to governance and policy considerations, including accountability, fairness in resource allocation, ethical constraints on autonomous decision-making, and the sustainability of learning systems under resource scarcity. Through cross-domain comparisons with other large-scale multi-agent systems, the paper identifies key design principles for building resilient and scalable swarm navigation systems. The analysis concludes with forward-looking perspectives on integrating human oversight, regulatory frameworks, and continual learning mechanisms to ensure safe and equitable operation in real-world disaster scenarios. This work provides a foundational reference for researchers and practitioners seeking to deploy cooperative deep reinforcement learning in high-stakes socio-technical environments.
References
1. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
2. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
3. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2016). Continuous control with deep reinforcement learning. In Proceedings of the 4th International Conference on Learning Representations (ICLR).
4. Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in Neural Information Processing Systems 30 (NeurIPS), 6379–6390.
5. Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018). Counterfactual multi-agent policy gradients. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2974–2982.
6. Rashid, T., Samvelyan, M., Schroeder, C., Farquhar, G., Foerster, J., & Whiteson, S. (2018). QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning. In Proceedings of the 35th International Conference on Machine Learning (ICML), 4295–4304.
7. Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J. P., Jaderberg, M., ... & Silver, D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354. https://doi.org/10.1038/s41586-019-1724-z
8. Sunehag, P., Lever, G., Gruslys, A., Czarnecki, W. M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J. Z., Tuyls, K., & Graepel, T. (2018). Value-decomposition networks for cooperative multi-agent learning based on team reward. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2085–2087.
9. Sukhbaatar, S., Szlam, A., & Fergus, R. (2016). Learning multiagent communication with backpropagation. In Advances in Neural Information Processing Systems 29 (NeurIPS), 2244–2252.
10. Erdelj, M., Natalizio, E., Chowdhury, K. R., & Akyildiz, I. F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Communications Magazine, 55(10), 132–138. https://doi.org/10.1109/MCOM.2017.1700163
11. Huang, S., Ontañón, S., & Brafman, R. I. (2019). Agent modeling for cooperative multi-agent reinforcement learning. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 1331–1339.
12. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (NeurIPS), 5998–6008.
13. Wolpert, D. H., & Tumer, K. (2002). Optimal payoff functions for members of collectives. In Advances in Neural Information Processing Systems 14 (NeurIPS), 679–686.
14. Devlin, S., & Kudenko, D. (2012). Dynamic potential-based reward shaping. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 745–752.
15. Hughes, E., Leibo, J. Z., Phillips, M. G., Tuyls, K., Dueñez-Guzman, E., García Castañeda, A., Dunning, I., Zhu, T., McKee, K. R., Koster, R., Roff, H., & Graepel, T. (2018). Inequity aversion improves cooperation in intertemporal social dilemmas. In Advances in Neural Information Processing Systems 31 (NeurIPS), 3326–3336.
16. Zhao, W., Queralta, J. P., & Westerlund, T. (2020). Sim-to-real transfer in deep reinforcement learning for robotics: A survey. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 737–744.
17. Liu, Z., Wang, J., & Xu, Y. (2021). Energy-aware multi-UAV cooperative search using deep reinforcement learning. IEEE Access, 9, 125842–125854. https://doi.org/10.1109/ACCESS.2021.3110897
18. Mao, H., Zhang, Z., Xiao, Z., & Gong, Z. (2020). Learning to communicate with deep multi-agent reinforcement learning in partially observable environments. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 7196–7203.
19. Pinto, L., Andrychowicz, M., Welinder, P., Zaremba, W., & Abbeel, P. (2017). Asymmetric actor critic for image-based robot learning. In Proceedings of the Robotics: Science and Systems (RSS).
20. García, J., & Fernández, F. (2015). A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research, 16(1), 1437–1480.
21. Johnson, M., & Vera, A. H. (2020). Human-autonomy teaming: A review and future directions. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64(1), 1440–1444.
22. Fulton, N., & Platzer, A. (2018). Safe reinforcement learning via formal methods. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 3110–3117.
23. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 3645–3650.
24. Zhu, H., Gupta, O., & Grosu, R. (2021). Scalable multi-agent reinforcement learning via factored value functions. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 1690–1692.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
