無人航空機への適用に向けた深層強化学習（Proximal Policy Optimization）によるスラストベクタリングを用いた一軸姿勢制御

抄録

Deep reinforcement learning is applied to the pitch control of the UAV equipped with electric ducted fans and thrust vectoring. A controller is trained to maintain the target attitude even when the system model is uncertain. Through the uniaxial rotation experiment, it was shown that the attitude can be controlled by the deep reinforcement learning-based approach with domain randomization.