Paper Detail
Caden Chandra, Jerry Ng
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.
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@article{chandra2026multi,
title = {Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response},
author = {Caden Chandra and Jerry Ng},
year = {2026},
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 },
url = {https://arxiv.org/abs/2609.10433},
keywords = {cs.RO, cs.LG},
eprint = {2609.10433},
archiveprefix = {arXiv},
}
{}