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Lyapunov Framework Enhances Learning in Weakly-Coupled MDPs

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a novel Lyapunov-based framework to analyze the sample complexity of learning in weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs). This approach offers a more efficient method for learning near-optimal policies compared to naive reductions, achieving polynomial sample and computational complexities. The framework establishes finite-sample PAC guarantees with improved optimality gaps and introduces a fine-grained perturbation analysis for linear programming relaxations as a key technical contribution. AI

IMPACT Introduces a novel theoretical framework that could lead to more efficient AI learning algorithms for sequential decision-making problems.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for analyzing sample complexity in specific types of Markov decision processes.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Lyapunov Framework Enhances Learning in Weakly-Coupled MDPs

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The cluster contains an academic paper detailing a new theoretical framework for analyzing sample complexity in specific types of Markov decision processes.
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COVERAGE [3]

  1. arXiv stat.ML TIER_1 English(EN) · Tianhao Wu, Matthew Zurek, Weina Wang, Qiaomin Xie ·

    Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

    arXiv:2606.14095v1 Announce Type: cross Abstract: We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds a…

  2. arXiv stat.ML TIER_1 English(EN) · Qiaomin Xie ·

    Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

    We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds as the state-action space is exponentially large in…

  3. arXiv stat.ML TIER_1 English(EN) · Qiaomin Xie ·

    Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

    We study the sample complexity of learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits (RBs) under a generative model. Naive reduction to a tabular MDP leads to high complexity bounds as the state-action space is exponentially large in…

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