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arXiv Machine Learning By Miguel Leiva-V\'elez, Nicolas Gaston-Rozado, Andrea Scorsoglio, Hodei Urrutxua, V\'ictor Rodr\'iguez-Fern\'andez

Adversarial Reinforcement Learning for Robust Planetary Landing

Read the original on arXiv Machine Learning →

The paper presents a method for improving the robustness of deep reinforcement learning (DRL) policies used in planetary landing by employing adversarial reinforcement learning. A secondary agent generates physically constrained perturbations that challenge the landing controller, creating an asymmetric zero‑sum game. Training is conducted in three stages—baseline learning, stochastic robustness enhancement, and iterative adversarial optimization—showing that adversarial disturbances uncover vulnerabilities missed by conventional randomization and hint at better generalization to unseen perturbations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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