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New algorithm offers efficient solutions for Markov Decision Processes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel algorithm for efficiently solving Markov Decision Process (MDP) problems that utilize function approximations. This new method is based on a linear programming reformulation and iteratively resolves a reduced linear system as more transition samples become available. The algorithm achieves an instance-dependent objective shortfall and constraint residual, with a theoretical guarantee of $O(1/\sqrt{N})$ that is independent of certain problem parameters. AI

IMPACT This research could lead to more efficient decision-making in complex systems, potentially impacting areas like robotics and reinforcement learning.

RANK_REASON This is a research paper detailing a new algorithm for solving Markov Decision Processes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New algorithm offers efficient solutions for Markov Decision Processes

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This is a research paper detailing a new algorithm for solving Markov Decision Processes. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiashuo Jiang, Yinyu Ye, Yiming Zong ·

    Adaptive Resolving Methods for Markov Decision Processes with Function Approximations

    arXiv:2505.12037v2 Announce Type: replace Abstract: Learning the optimal policy for Markov decision process problems (MDPs) from samples is a fundamental problem in online and data-driven decision-making. Function approximations are usually deployed to handle large or infinite st…

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