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English(EN) Reachability-Informed Reinforcement Learning for Multi-Impulse Interplanetary Transfers

新的强化学习方法优化星际转移

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为可达性分析感知强化学习（RARL）的新方法来设计星际航天器轨迹。该方法将强化学习与可达性图相结合，以选择中间航点，然后指导经典的轨道力学技术进行机动规划。在地球-火星基准测试中，RARL 实现的平均机动成本接近顺序凸规划，并在各种出发条件下展示了显著的策略重用性，独立训练的策略成功完成了所有测试出发任务，且未违反脉冲约束。 AI

影响 这项研究可能导致更高效、可重用的自动化太空任务规划系统。

排序理由 详细介绍航天器轨迹设计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的强化学习方法优化星际转移

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详细介绍航天器轨迹设计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yashdeep Chaudhary, Roberto Armellin, Harry Holt ·

    面向可达性感知的多脉冲行星际转移强化学习

    arXiv:2610.01344v1 Announce Type: cross Abstract: Reinforcement learning offers the prospect of a reusable sequential decision-making mechanism for spacecraft trajectory design, motivating policy interfaces that connect learned decisions to the underlying maneuver geometry. This …

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