English(EN) Reinforcement Learning with Multi-Step Transition Predictions: A Bellman Theory

新的贝尔曼理论通过多步预测增强强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一个新的强化学习框架，通过结合多步转移预测来提高效率。该方法避免了指数级状态空间增长，采用了块反馈协议，其中决策基于 K 周期块预测进行调整。该研究引入了贝叶斯贝尔曼方程来量化预测信息的价值，并提出了一种名为 BF--BOLA 的算法，用于学习具有已建立样本复杂度界限的近最优策略。 AI

影响这项研究通过改进转移预测的利用方式，有望带来更高效、更有效的强化学习系统。

排序理由该集群包含一篇研究论文，详细介绍了强化学习的新理论框架和算法。[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) · Chenbei Lu, Zaiwei Chen, Tongxin Li, Chenye Wu, Adam Wierman · 2026-10-27 04:00

基于多步转移预测的强化学习：贝尔曼理论

arXiv:2610.32887v1 Announce Type: new Abstract: Multi-step predictions of future transition realizations can improve reinforcement learning, but their value remains difficult to quantify. Directly including these predictions in the state can lead to exponential state-space growth…

本文如何被排名

报道来源 [1]

基于多步转移预测的强化学习：贝尔曼理论

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