Propulsive landing of launchers’ first stages with Deep Reinforcement Learning

The planetary landing problem is gaining relevance in the space sector, spanning a wide range of applications from unmanned probes landing on other planetary bodies to reusable first and second stages of launcher vehicles. In the existing methodology there is a lack of flexibility in handling complex non-linear dynamics, in particular in the case of non-convexifiable constraints. It is therefore crucial to assess the performance of novel techniques and their advantages and disadvantages. The purpose of this work is the development of an integrated 6-DOF guidance and control approach based on reinforcement learning of deep neural network policies for fuel-optimal planetary landing control, specifically with application to a launcher first-stage terminal landing, and the assessment of its performance and robustness. 3-DOF and 6-DOF simulators are developed and encapsulated in MDP-like (Markov Decision Process) industry-standard compatible environments. Particular care is given in thoroughly shaping reward functions capable of achieving the landing both successfully and in a fuel-optimal manner. A cloud pipeline for effective training of an agent using a PPO reinforcement learning algorithm to successfully achieve the landing goal is developed.