On four spacecraft guidance problems, reinforcement learning trains networks that are more robust to disturbances than behavioural cloning, but behavioural cloning matches optimal control when the expert data is accurate.
A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control
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abstract
The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.
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Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks
On four spacecraft guidance problems, reinforcement learning trains networks that are more robust to disturbances than behavioural cloning, but behavioural cloning matches optimal control when the expert data is accurate.