This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). Computational guidance algorithms can deal with emergencies during flight and improve the success rate of missions, and most of the current computational guidance algorithms are based on optimal control, whose calculation efficiency cannot be guaranteed. However, guidance-based DNN has high computational efficiency. A reward function that satisfies the flight process and terminal constraints is designed, then the mapping from state to control is trained by the state-of-the-art proximal policy optimization algorithm. The results of the proposed algorithm are compared with results obtained by the guidance-based optimal control, showing the effectiveness of the proposed algorithm. In addition, an engine failure numerical experiment is designed in this manuscript, demonstrating that the proposed algorithm can guide the launch vehicle to a feasible rescue orbit.

中文翻译：

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运载火箭上级计算制导的强化学习

本手稿研究了使用强化学习方法来引导运载火箭和基于深度神经网络 (DNN) 的计算制导算法。计算制导算法可以处理飞行过程中的突发事件，提高任务的成功率，而目前的计算制导算法大多基于最优控制，计算效率无法保证。然而，基于指导的 DNN 具有较高的计算效率。设计一个满足飞行过程和终端约束的奖励函数，然后通过最先进的近端策略优化算法训练从状态到控制的映射。将所提出算法的结果与基于制导的最优控制得到的结果进行比较，显示了所提出算法的有效性。此外，本文还设计了发动机故障数值实验，证明所提出的算法可以引导运载火箭进入可行的救援轨道。