深層強化学習による自律航空機群の分散型山火事監視
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning
深層強化学習を用いて複数の固定翼自律機を分散制御し、山火事の拡大を追跡・監視する手法を提案し、既存のオンライン再cedingホライズン制御より優れることを示した。
著者: Kyle D. Julian, Mykel J. Kochenderfer
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
