English(EN) Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain

新研究探讨平均奖励鲁棒马尔可夫决策过程的贝尔曼最优性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

本文深入探讨了平均奖励鲁棒马尔可夫决策过程（MDP）的理论基础，这是一个复杂的序贯决策领域。研究聚焦于恒定增益设置，以建立一个通用框架，分析鲁棒贝尔曼方程及其与最优平均奖励的联系。研究确定了该方程的解准确表示最优平均奖励和稳态策略的条件，从而推进了在运行环境中的长期平均标准的动态规划理论。 AI

影响扩展了在长期平均标准下进行鲁棒决策的动态规划理论。

排序理由学术论文发表在arXiv上，详细介绍了马尔可夫决策过程的理论进展。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Shengbo Wang, Nian Si · 2026-10-21 04:00

平均奖励鲁棒马尔可夫决策过程的 Bellman 最优性与常数增益

arXiv:2509.14203v4 Announce Type: replace-cross Abstract: Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. Long-run a…