English(EN) SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models

研究人员使用扩散模型进行合成演示的模仿学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-30 04:00

研究人员开发了SD2AIL，一种新颖的对抗性模仿学习方法，该方法利用扩散模型生成合成专家演示。该方法旨在通过增加AI生成的示例来克服收集大量真实世界专家数据的挑战。该系统还采用优先回放策略来关注最有价值的演示，在Hopper环境等模拟任务上显示出显著的性能提升。 AI

影响通过减少对真实世界专家数据的依赖来增强模仿学习，有可能加速复杂模拟中的策略优化。

排序理由这是一篇详细介绍模仿学习新方法的学术论文。

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

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arXiv cs.LG TIER_1 English(EN) · Pengcheng Li, Qiang Fang, Tong Zhao, Yixing Lan, Xin Xu · 2026-04-30 04:00

SD2AIL：通过扩散模型从合成演示中进行对抗性模仿学习

arXiv:2512.18583v2 Announce Type: replace Abstract: Adversarial Imitation Learning (AIL) is a dominant framework in imitation learning that infers rewards from expert demonstrations to guide policy optimization. Although providing more expert demonstrations typically leads to imp…