# Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response

arXiv: https://arxiv.org/abs/2609.10433 · HTML: https://mlsanity.com/paper/2609.10433/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 9 Sep 2026.

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

- [Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits](https://mlsanity.com/paper/2411.18704/) (2024)
- [A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model](https://mlsanity.com/paper/2507.00761/) (2025)
- [Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems](https://mlsanity.com/paper/2506.12710/) (2025)
- [Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2402.14569/) (2024)
- [URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2505.20175/) (2025)
- [Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning](https://mlsanity.com/paper/2506.02485/) (2025)
- [SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning](https://mlsanity.com/paper/2507.00005/) (2025)

## Abstract (arXiv)

This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfire environments. Results show that agents learn increasingly stable and effective behaviors over time, as demonstrated by converging loss trends, improved reward signals, and more consistent navigation patterns such as fire-boundary tracking. Overall, these findings highlight the potential of deep reinforcement learning (DRL) based UAV systems for autonomous wildfire monitoring and suggest that environmental structure and reward design influence policy effectiveness.