# Strategic Coordination of Drones via Short-term Distributed Optimization and Long-term Reinforcement Learning

arXiv: https://arxiv.org/abs/2311.09852 · HTML: https://mlsanity.com/paper/2311.09852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2023.

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

- [Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning](https://mlsanity.com/paper/2509.18088/) (2025)

## Abstract (arXiv)

This paper addresses the problem of autonomous task allocation by a swarm of autonomous, interactive drones in large-scale, dynamic spatio-temporal environments. When each drone independently determines navigation, sensing, and recharging options to choose from such that system-wide sensing requirements are met, the collective decision-making becomes an NP-hard decentralized combinatorial optimization problem. Existing solutions face significant limitations: distributed optimization methods such as collective learning often lack long-term adaptability, while centralized deep reinforcement learning (DRL) suffers from high computational complexity, scalability and privacy concerns. To overcome these challenges, we propose a novel hybrid optimization approach that combines long-term DRL with short-term collective learning. In this approach, each drone uses DRL methods to proactively determine high-level strategies, such as flight direction and recharging behavior, while leveraging collective learning to coordinate short-term sensing and navigation tasks with other drones in a decentralized manner. Extensive experiments using datasets derived from realistic urban mobility demonstrate that the proposed solution outperforms standalone state-of-the-art collective learning and DRL approaches by 27.83% and 23.17% respectively. Our findings highlight the complementary strengths of short-term and long-term decision-making, enabling energy-efficient, accurate, and sustainable traffic monitoring through swarms of drones.