English(EN) FARL: A Fully Automated Pipeline of RL Interface and Solver Design

FARL框架自动化强化学习流程设计

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

研究人员开发了FARL，一个旨在自动化整个强化学习（RL）设计过程的框架。FARL解决了联合搜索观测程序、奖励函数、学习算法和训练配置的挑战，这些通常依赖于人类专业知识。该系统利用训练统计数据和观察到的行为来诊断和纠正失败，在复杂的导航、运动和操作任务上优于现有的自动化基线。在一项实验中，FARL在Adroit Hand Relocate任务上，在500万次训练步数内达到了97.1%的成功率，显著超过了其他评估方法。 AI

影响自动化复杂的强化学习流程设计，有望加速机器人和人工智能领域的研究与应用开发。

排序理由详细介绍用于自动化强化学习流程设计的新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Tianyu Jin, Liuyu Xiang, Chaowen Yang, Xingyang Chang, Pei Pei Li, Zhaofeng He · 2026-10-15 04:00

FARL：RL接口和求解器设计的全自动流水线

arXiv:2610.15691v1 Announce Type: new Abstract: Existing reinforcement learning (RL) largely focuses on optimizing learning algorithms within a pre-defined task formulation, while applying RL to new tasks still relies heavily on human expertise to design the learning pipeline: wh…

本文如何被排名

报道来源 [1]

FARL：RL接口和求解器设计的全自动流水线

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