AI 中文总结
本研究提出深度强化学习框架训练无人机智能体在模拟野火环境中导航监测，实验显示其行为稳定有效，凸显DRL无人机系统在自主野火监测中的潜力。
AI 中文摘要
本研究开发了一个深度强化学习框架，用于训练无人机（UAV）智能体在模拟野火环境中进行导航和监测。结果表明，智能体随时间推移学会了越来越稳定和有效的行为，这体现在收敛的损失趋势、改进的奖励信号以及更一致的导航模式（如火线边界跟踪）上。总体而言，这些发现凸显了基于深度强化学习（DRL）的无人机系统在自主野火监测方面的潜力，并表明环境结构和奖励设计会影响策略有效性。
英文摘要
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.
