Reinforcement Learning for Computational Guidance of Launch Vehicle Upper Stage

Abstract

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.

A Real-Time and Optimal Hypersonic Entry Guidance Method Using Inverse Reinforcement Learning

“…RL has also shown promising results in addressing aerospace problems [25,26]. In comparison to traditional guidance algorithms, RL-based guidance algorithms exhibit strong antidisturbance capabilities and real-time performance [27][28][29][30]. Ref.…”