多智能体强化学习用于无人机火灾监测
Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response
无人机用强化学习自动监测火灾，能跟踪火线，比人工更安全高效。
该研究开发了深度强化学习框架，训练无人机在模拟火灾环境中导航和监测。结果显示，随着训练时间增加，无人机表现出越来越稳定和有效的行为，损失函数收敛，奖励信号改善，导航模式更加一致，如火灾边界跟踪。研究表明，环境结构和奖励设计影响策略有效性。
Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response
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.