mlsanityData to 14 Oct 2026

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

arXiv 2609.10433 ↗First posted 9 Sep 2026cs.RO cs.LGcited by 0

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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.

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.

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7 of 15 references.

References · 7

  1. 2024Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefitscited by 71
  2. 2025A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Modelcited by 11
  3. 2025Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systemscited by 7
  4. 2024Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learningcited by 3
  5. 2025URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learningcited by 1
  6. 2025Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learningcited by 1
  7. 2025SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learningcited by 1

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  1. 2026FIRE-VLM: A Vision-Language-Driven Reinforcement Learning Framework for UAV Wildfire Tracking in a Physics-Grounded Fire Digital Twin
  2. 2024PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments
  3. 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring
  4. 2024Vision-Based Deep Reinforcement Learning of UAV Autonomous Navigation Using Privileged Information
  5. 2025Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
  6. 2024Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement
  7. 2024Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)
  8. 2024Navigation in a simplified Urban Flow through Deep Reinforcement Learning