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New sub-quadratic method improves bisimulation metric computation for MDPs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel sub-quadratic method for calculating bisimulation metrics in Markov decision processes (MDPs). This new approach utilizes approximate nearest neighbor (ANN) indexing to efficiently select state pairs for updates, significantly reducing the computational cost compared to traditional quadratic methods. The technique provides coverage-augmented guarantees, ensuring that global error is controlled even when not all state pairs are updated, and offers computable two-sided certificates to verify the accuracy of the metric. AI

RANK_REASON The cluster contains a research paper detailing a new algorithmic method for computing bisimulation metrics in Markov decision processes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New sub-quadratic method improves bisimulation metric computation for MDPs

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The cluster contains a research paper detailing a new algorithmic method for computing bisimulation metrics in 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) · Ibne Farabi Shihab, Joyanta Jyoti Mondal ·

    Sub-Quadratic Bisimulation Metrics via Approximate Nearest Neighbors: Coverage-Augmented Guarantees and Computable Two-Sided Certificates

    arXiv:2608.06762v1 Announce Type: new Abstract: Bisimulation metrics quantify behavioral similarity in Markov decision processes, but their Wasserstein fixed-point operator updates every state pair and incurs quadratic pairwise work. We give a certificate-carrying sub-quadratic m…

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