English(EN) V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms

新的V-SHARE算法为强化学习提供可证明的收敛性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员推出了一种新颖的基于优势的价值学习算法V-SHARE，用于强化学习，可增强跨动作的信息共享。该方法将动作价值函数分解为状态依赖值和动作特定优势，并借鉴了Q学习等现有技术的有效性。V-SHARE首次对这类算法进行了全面的理论表征，提供了渐近收敛和有限样本保证，并解决了异步Q学习中稀有动作的挑战。 AI

影响引入了一种理论上可靠的算法，可以提高强化学习方法的效率和理解。

排序理由该条目是一篇学术论文，详细介绍了一种新的强化学习算法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Kintan Saha, Hamed Masoudi, Xinyi Sheng, Dominik Baumann, Joni Pajarinen, Arsenii Mustafin · 2026-10-16 04:00

V-SHARE：基于优势算法的可证明收敛保证

arXiv:2610.21448v1 Announce Type: cross Abstract: In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the action-value function into a state-dependent value component and action-specific ad…

本文如何被排名

报道来源 [1]

V-SHARE：基于优势算法的可证明收敛保证

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