Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response
Researchers trained multi-agent deep reinforcement learning UAV agents for autonomous wildfire monitoring, with converging policies tracking fire boundaries in simulation.
The study develops a deep reinforcement learning framework for training UAV agents to navigate and monitor simulated wildfire environments. Agents showed increasingly stable and effective behavior over time, evidenced by converging loss trends, improved rewards, and consistent navigation patterns such as fire-boundary tracking. The findings highlight DRL-based UAV potential for autonomous wildfire monitoring and show that environmental structure and reward design influence policy effectiveness.
- Multi-agent DRL framework for autonomous wildfire UAV exploration
- Agents converge to stable fire-boundary tracking behaviors
- Convergence shown via loss trends and improved reward signals
- Environmental structure and reward design shape policy effectiveness
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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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.10433