Autonomous Lunar Lander, Deep Reinforcement Learning for Control application

Matteo Stoisa

Autonomous Lunar Lander, Deep Reinforcement Learning for Control application.

Rel. Elena Maria Baralis, Lorenzo Feruglio, Mattia Varile, Luca Romanelli. Politecnico di Torino, Master of science program in Computer Engineering, 2021

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Abstract

The thesis aims to analyze the application of a Deep Reinforcement Learning algorithm to a case study in the field of control. The algorithm chosen is Proximal Policy Optimization, which in recent years has reached the state of the art in various fields of application; the control problem taken into consideration is the powered descent phase of a lander and the subsequent landing in a predetermined area, the Apollo 11 mission was taken as a guideline for some aspects in the creation of the model. The Unity framework was used for the modeling and simulation part, the ML-Agents library for the management of the DRL part.

The implementation of the fidelity of the model was taken with an incremental approach, which allowed to gradually understand and deal with the critical issues given that as the physical complexity of the problem increases, the difficulty in achieving the desired result increases considerably