深入审视高噪声数据集中的优势过滤行为克隆
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets
AI总结：
针对高噪声离线数据集，本文利用优先经验采样从海量低性能样本中筛选专家演示，使优势过滤行为克隆在专家动作约占1/65时仍学到最先进策略。
摘要：
近期的离线强化学习方法已能够从固定的经验数据集中学习高性能策略。一种尤为有效的方法是先识别再模仿最优决策策略。本文评估该方法扩展到几乎完全由次优噪声构成的海量数据集时的能力。我们在一个定制基准上进行全面研究，识别出从高噪声数据集中学习所涉及的若干关键挑战。我们重新利用优先经验采样，在数百万条低性能样本中定位专家级演示。该改进使离线智能体能够在专家动作占比接近1:65的数据集上，于基准任务中学习达到最先进水平的策略。
英文摘要：
Recent Offline Reinforcement Learning methods have succeeded in learning high-performance policies from fixed datasets of experience. A particularly effective approach learns to first identify and then mimic optimal decision-making strategies. Our work evaluates this method's ability to scale to vast datasets consisting almost entirely of sub-optimal noise. A thorough investigation on a custom benchmark helps identify several key challenges involved in learning from high-noise datasets. We re-purpose prioritized experience sampling to locate expert-level demonstrations among millions of low-performance samples. This modification enables offline agents to learn state-of-the-art policies in benchmark tasks using datasets where expert actions are outnumbered nearly 65:1.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
