# Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2303.01150 · HTML: https://mlsanity.com/paper/2303.01150/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2023.

## Built on by (most cited first, 4 of 4)

- [Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16967/) (2024)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [Hardware-Enforced Semantic Coordination for Safety-Critical Real-Time Autonomous Systems](https://mlsanity.com/paper/2607.02376/) (2026)
- [Sensor-Driven Mission Synthesis for UAV/UGV Swarms: A TB-CSPN Coordination Architecture with Hardware-Enforced Safety](https://mlsanity.com/paper/2608.14306/) (2026)

## Abstract (arXiv)

Efficient aerial data collection is important in many remote sensing applications. In large-scale monitoring scenarios, deploying a team of unmanned aerial vehicles (UAVs) offers improved spatial coverage and robustness against individual failures. However, a key challenge is cooperative path planning for the UAVs to efficiently achieve a joint mission goal. We propose a novel multi-agent informative path planning approach based on deep reinforcement learning for adaptive terrain monitoring scenarios using UAV teams. We introduce new network feature representations to effectively learn path planning in a 3D workspace. By leveraging a counterfactual baseline, our approach explicitly addresses credit assignment to learn cooperative behaviour. Our experimental evaluation shows improved planning performance, i.e. maps regions of interest more quickly, with respect to non-counterfactual variants. Results on synthetic and real-world data show that our approach has superior performance compared to state-of-the-art non-learning-based methods, while being transferable to varying team sizes and communication constraints.