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English(EN) Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?

研究质疑强化学习微调中的Q函数预训练

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新的研究论文质疑在强化学习（RL）中微调策略时预训练Q函数的必要性。研究发现，由于预训练的Q函数与在线微调最终收敛的Q函数之间存在不匹配，朴素的Q函数预训练通常比随机初始化带来的优势很小。为了解决这个问题，研究人员提出了策略集成初始化（IPE）方法，该方法使用来自不同策略的汇集数据来引导Q函数学习，在连续控制基准测试中显示出平均1.26倍的微调性能提升。 AI

影响 挑战强化学习微调的传统观念，可能导致更复杂的控制任务的更有效训练方法。

排序理由 该集群包含一篇详细介绍强化学习新研究发现的学术论文。

在 arXiv cs.LG 阅读 →

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研究质疑强化学习微调中的Q函数预训练

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Perry Dong, Ron Polonsky, Dorsa Sadigh, Chelsea Fin ·

    在线强化学习微调真的需要预训练Q函数吗？

    arXiv:2607.27203v1 Announce Type: new Abstract: Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) this raises a natural question: given a pretrained policy, should the Q-function be…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    在线强化学习微调真的需要预训练Q函数吗？

    Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) this raises a natural question: given a pretrained policy, should the Q-function be pretrained on offline data too? Conventional wi…

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