English(EN) Accelerating Q-learning through Efficient Value-Sharing across Actions

新的均值扩展层加速强化学习价值共享

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-30 04:00

研究人员开发了一种称为均值扩展层的新方法，用于加速Q学习等强化学习算法中的动作值学习。该层在单个状态内高效地跨不同动作共享价值信息，而不是独立更新每个状态-动作对。通过学习动作值的低范数表示，该方法在集成到深度Q网络和隐式分位数网络后，在Atari游戏上表现出改进的性能，减少了价值高估，并增加了动作差距。 AI

影响这种新方法可能导致在复杂环境中更有效地训练AI代理。

排序理由详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Prabhat Nagarajan, Brett Daley, Martha White, Marlos C. Machado · 2026-06-30 04:00

通过高效的跨动作值共享加速Q学习

arXiv:2606.29806v1 Announce Type: cross Abstract: Action-values are foundational to many control algorithms such as Q-learning. Therefore learning action-values efficiently is central to reinforcement learning (RL). However, learning them can be slow, requiring many updates to mo…

本文如何被排名

报道来源 [1]

通过高效的跨动作值共享加速Q学习

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