Adversarial RL enhances planetary landing control robustness

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种对抗性强化学习方法，以增强航空航天控制任务（如行星着陆）的深度强化学习（DRL）策略的鲁棒性。该方法涉及一个次级代理，该代理生成物理约束的、具有挑战性的非标称条件，从而创建一个不对称的序贯零和博弈。初步实验表明，这些对抗性干扰可以揭示传统随机化方法所忽略的漏洞，预示着未来应用中鲁棒性和泛化能力的提高。 AI

影响这项研究可能为太空探索等挑战性环境中的自主系统带来更高的可靠性。

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Adversarial RL enhances planetary landing control robustness

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Miguel Leiva-V\'elez, Nicolas Gaston-Rozado, Andrea Scorsoglio, Hodei Urrutxua, V\'ictor Rodr\'iguez-Fern\'andez · 2026-10-15 04:00

Adversarial Reinforcement Learning for Robust Planetary Landing

arXiv:2610.20252v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to improve robustness. However, typical stochastic pertu…

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Adversarial Reinforcement Learning for Robust Planetary Landing

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