English(EN) Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging

新的LP-BTS规划架构解决了动态动作空间问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一种名为LP-BTS的新规划架构，专为具有大型、动态动作空间的复杂序贯决策场景设计。该系统利用图建议策略来缩小候选动作范围，并利用学习到的价值评论器来评估潜在结果。实验表明，在移动充电模拟的生存百分比和行驶距离方面，LP-BTS的性能显著优于均匀采样和直接策略选择方法。 AI

影响这项研究为复杂AI系统中的规划引入了一种新颖的方法，有可能提高动态环境中的效率。

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arXiv cs.AI TIER_1 English(EN) · Liang-Ching Tao, Pi-Chung Wang · 2026-09-16 04:00

Large Dynamic Action Spaces中的学习引导规划：面向一对多移动充电的预算树搜索

arXiv:2609.17429v1 Announce Type: cross Abstract: Many learned sequential decision systems map the current state directly to an action. That shortcut becomes brittle when candidate actions are numerous, geometrically structured, and rebuilt with the state. One-to-many mobile char…