新算法为低秩约束CMDPs提供理论保证

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了两种新算法，REP-PD-TEG和REP-PD-H-TEG，用于具有连续状态空间的低秩约束马尔可夫决策过程（CMDPs）中的表示学习。REP-PD-TEG通过最大似然学习转移特征，然后使用乐观的拉格朗日目标进行规划，从而在软累积违规设置下实现最优遗憾界限。对于更严格的硬累积违规，REP-PD-H-TEG采用奖励修正的低置信度效用证书和稳定的对偶搜索，以确保每集的可行性，同时提供遗憾界限和硬违规界限。这些方法代表了首个在低秩连续状态CMDPs方面提供理论保证的方法，同时提供软累积保证和经过认证的硬违规控制。 AI

影响这些算法在理论理解和实际应用方面推进了强化学习在复杂、受限环境中的应用。

排序理由该集群包含一篇详细介绍特定类型马尔可夫决策过程新算法的研究论文。[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) · Chenhao Zhou, Chao Zhang, Wensong Bai, Hanbin Zhao, Hui Qian · 2026-10-23 04:00

Primal-Dual Representation Learning for Low-Rank Constrained MDPs

arXiv:2610.30756v1 Announce Type: new Abstract: Representation learning has substantially improved the efficiency of unconstrained continuous-state Markov Decision Processes (MDPs), but its extension to Constrained MDPs (CMDPs) is difficult because the transition representation m…

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Primal-Dual Representation Learning for Low-Rank Constrained MDPs

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