arXiv Machine Learning July 1, 2026 By Alexander Fabisch, Melvin Laux, Mariela De Lucas \'Alvarez, Edoardo Caroselli, Julian Theis

Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry

Read the original on arXiv Machine Learning →

arXiv:2606. 31291v1 Announce Type: new Abstract: Deep reinforcement learning has the potential to solve attitude control problems more adaptively, precisely, and robustly by handling nonlinear dynamics, uncertainties, and failure cases more effectively than traditional attitude control approaches.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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