深層強化学習アルゴリズムによる宇宙機の最適軌道設計

抄録

This study focuses on the suboptimal spacecraft orbit design using a deep reinforcement learning (DRL) method. In the optimal orbit design issues, it is not easy to solve the nonlinear problems and we have many difficulties to solve them. However, reinforcement learning can be applied regardless of linear or nonlinear. Therefore, quasi-optimized solutions can be achieved more simply. In this paper, we show the effectiveness of the DRL algorithm in the orbit design in the case of super synchronous and spiral orbital transfer problems.