引导流策略：在离线强化学习中学习高价值动作
Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
AI总结：
引导流策略通过结合多步流匹配与提炼的演员，有效区分高价值动作，从而在多个基准测试中实现卓越性能。
摘要：
离线强化学习通常依赖于行为正则化，以迫使策略保持接近数据集分布。然而，此类方法未能在正则化组件中区分高价值和低价值动作。我们引入了引导流策略（GFP），它将多步流匹配策略与一个提炼的一步演员相结合。演员通过加权行为克隆引导流策略，专注于从数据集中克隆高价值动作，而不是无差别地模仿所有状态-动作对。反过来，流策略会约束演员保持与数据集最佳转换一致，同时最大化批评器。这种相互引导使GFP在OGBench、Minari和D4RL基准的144个状态和像素基任务中实现了最先进的性能，在次优化数据集和具有挑战性的任务上取得了显著提升。
英文摘要：
Offline reinforcement learning often relies on behavior regularization that enforces policies to remain close to the dataset distribution. However, such approaches fail to distinguish between high-value and low-value actions in their regularization components. We introduce Guided Flow Policy (GFP), which couples a multi-step flow-matching policy with a distilled one-step actor. The actor directs the flow policy through weighted behavior cloning to focus on cloning high-value actions from the dataset rather than indiscriminately imitating all state-action pairs. In turn, the flow policy constrains the actor to remain aligned with the dataset's best transitions while maximizing the critic. This mutual guidance enables GFP to achieve state-of-the-art performance across 144 state and pixel-based tasks from the OGBench, Minari, and D4RL benchmarks, with substantial gains on suboptimal datasets and challenging tasks. Webpage: https://simple-robotics.github.io/publications/guided-flow-policy/
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。
