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New frameworks advance Markov Decision Processes for reinforcement learning · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers introduce advanced frameworks for Markov Decision Processes (MDPs), a key tool in reinforcement learning. The first paper, GRASP-MDP, addresses challenges in offline reinforcement learning by separating reward and transition dynamics, allowing for generalized linear models for rewards and better utilization of transition-only observations. The second paper focuses on robust average-reward MDPs, establishing minimax-optimal learning bounds and proposing plug-in reduction procedures that account for model uncertainty and achieve sample complexity rates dependent on state-action space and uncertainty levels. AI

IMPACT These advancements in reinforcement learning frameworks could lead to more robust and efficient decision-making in complex, uncertain environments.

RANK_REASON Two academic papers published on arXiv introducing new theoretical frameworks for Markov Decision Processes.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New frameworks advance Markov Decision Processes for reinforcement learning · 2 sources tracked

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Sinian Zhang, Kaicheng Zhang, Ziping Xu, Zongqi Xia, Jue Hou, Tianxi Cai, Doudou Zhou ·

    Generalized Linear Markov Decision Process

    arXiv:2506.00818v2 Announce Type: replace Abstract: Offline reinforcement learning for longitudinal studies often faces two linked challenges: rewards may be binary or bounded, and reward observations may be available only for a subset of trajectories or time points even when the…

  2. arXiv stat.ML TIER_1 English(EN) · Yuepeng Yang, Yuxin Chen, Yuejie Chi ·

    Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

    arXiv:2608.06545v1 Announce Type: cross Abstract: Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty. We study how many samples are necessary and sufficient to learn an $\varepsilon$-optimal robu…

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