多智能体强化学习用于野火响应中的自主 UAV 探索

Computer Science > Robotics arXiv:2609.10433v1 (cs) [Submitted on 9 Sep 2026] Title:Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response Authors:Caden Chandra, Jerry Ng View a PDF of the paper titled Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response, by Caden Chandra and 1 other authors View PDF HTML (experimental) Abstract: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. Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2609.10433 [cs.RO] (or arXiv:2609.10433v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.10433 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Caden Chandra [view email] [v1] Wed, 9 Sep 2026 16:45:19 UTC (7,011 KB) Full-text links: Access Paper: View a PDF of the paper titled Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response, by Caden Chandra and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)