English(EN) Schr\"odinger--F\"ollmer Actor--Critic: Diffusion Policy Improvement with Finite-Sample Analysis

新的SFAC方法通过扩散策略增强了强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员引入了Schrödinger--Föllmer Actor--Critic (SFAC)方法，这是一种用于离线到在线强化学习的新方法。SFAC利用条件扩散，通过采用minimax Bellman critic来估计动作价值函数，从而改进策略。该方法旨在解决扩散策略中从结果目标分布采样的挑战，并在连续控制任务上展示了改进的性能。 AI

影响引入了一种使用扩散模型改进强化学习策略的新方法，有可能提高AI从离线数据中学习的能力。

排序理由该集群包含一篇详细介绍强化学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yuling Jiao, Lican Kang, Jerry Zhijian Yang, Jincheng Ying · 2026-11-05 05:00

Schrödinger--Föllmer Actor--Critic: 有限样本分析的扩散策略改进

arXiv:2609.33239v3 Announce Type: replace-cross Abstract: Diffusion policies represent multimodal action distributions, but a $Q$-weighted update does not specify how to sample from the resulting target distribution. We propose Schr\"odinger--F\"ollmer Actor--Critic (SFAC), an of…

本文如何被排名

报道来源 [1]

Schrödinger--Föllmer Actor--Critic: 有限样本分析的扩散策略改进

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