Teams of autonomous unmanned aircraft can be used to monitor wildfires,
enabling firefighters to make informed decisions. However, controlling multiple
autonomous fixed-wing aircraft to maximize forest fire coverage is a complex
problem. The state space is high dimensional, the fire propagates
stochastically, the sensor information is imperfect, and the aircraft must
coordinate with each other to accomplish their mission. This work presents two
deep reinforcement learning approaches for training decentralized controllers
that accommodate the high dimensionality and uncertainty inherent in the
problem. The first approach controls the aircraft using immediate observations
of the individual aircraft. The second approach allows aircraft to collaborate
on a map of the wildfire's state and maintain a time history of locations
visited, which are used as inputs to the controller. Simulation results show
that both approaches allow the aircraft to accurately track wildfire expansions
and outperform an online receding horizon controller. Additional simulations
demonstrate that the approach scales with different numbers of aircraft and
generalizes to different wildfire shapes.

本文研究了利用自主无人机团队监测野火的方法，通过使用两种深度强化学习方法训练分散控制器，可以有效地追踪野火的扩散，这两种方法在不同数量的飞行器和不同野火形状下都有较好的表现，模拟结果表明，这两种方法优于在线递归视角控制器。