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What is SR245 about?

This document discusses the application of deep reinforcement learning (RL) for optimizing trajectories and fuel efficiency in N-body systems, addressing limitations of classical trajectory design methods. The research aims to determine if an RL agent can generalize across multiple planetary targets while adapting to uncertainties, with hypotheses focused on fuel-time rewards and domain randomization. The study includes a detailed experimental design and analysis framework to compare RL-generated trajectori

Author

3033399

Language

EN