1
Generalized Linear Markov Decision Process
提出广义线性马尔可夫决策过程统一框架，为强化学习理论分析提供新视角，值得关注。
arXiv:2506.00818v2 Announce Type: replace-cross Abstract: Offline reinforcement learning for longitudinal studies often faces two linked challenges: r…
2
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
提出贝尔曼-泰勒分数解码方法，解决状态依赖可行动作集下MDP的优化难题，为强化学习提供新视角。
arXiv:2606.10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state depe…
3
A Contractive Feedback Semantics for Reinforcement Learning
从封闭马尔可夫决策过程跳脱，用组合视角打造强化学习的新型收缩反馈语义。
arXiv:2605.24759v1 Announce Type: new Abstract: Discounted reinforcement learning is usually presented through Bellman equations on closed Markov deci…
4
Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes
提出首个在重尾MDP上同时实现随机与对抗环境最优遗憾的BoBW算法，突破保守局限。
arXiv:2602.01295v3 Announce Type: replace Abstract: We investigate episodic Markov Decision Processes with heavy-tailed losses (HTMDPs). Existing appr…