English(EN) Dissimilarity-Based Markov Decision Processes for Stabilizing State Distributions

新的DMDP框架稳定强化学习中的状态分布

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员引入了基于差异的马尔可夫决策过程（DMDPs），以稳定强化学习中的状态分布。该新框架旨在确保状态分布收敛到期望目标并保持稳定，超越了传统的期望奖励优化。该方法通过衡量当前分布与目标分布之间的差异来构建问题，建立渐近稳定性条件，并开发策略学习和验证方法。 AI

影响通过显式管理状态分布稳定性，这项研究可能带来更可靠和可预测的强化学习代理行为。

排序理由该集群包含一篇详细介绍新理论框架及其在强化学习中实现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Xun Shen, Yuepeng Wang, Daniyal Sajedi-Hosseini, Akifumi Wachi, Ken Kawano, Yoshihiko Fujisawa, Mehrshad Sadria, S\'ebastien Gros, Katsuki Fujisawa · 2026-10-22 04:00

Dissimilarity-Based Markov Decision Processes for Stabilizing State Distributions

arXiv:2610.29024v1 Announce Type: new Abstract: Reinforcement learning for stochastic systems typically optimizes expected cumulative rewards, while reliable and repeatable behavior also depends on how the state distribution evolves over time. Optimizing expected return alone doe…