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New algorithm MFRLC offers provably efficient representation learning for CMDPs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new model-free algorithm called MFRLC for learning representations in low-rank Constrained Markov Decision Processes (CMDPs). This algorithm aims to maximize expected cumulative rewards while adhering to constraints on cumulative utility. MFRLC uniquely combines Least-Squares Value Iteration with Upper Confidence Bound (LSVI-UCB) and primal-dual techniques, and it achieves sublinear regret and violation guarantees, independent of state space size. AI

IMPACT This research introduces a novel algorithm for representation learning in CMDPs, potentially improving decision-making in complex, constrained environments.

RANK_REASON The cluster contains a research paper detailing a new algorithm for a specific machine learning problem domain. [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 algorithm MFRLC offers provably efficient representation learning for CMDPs

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The cluster contains a research paper detailing a new algorithm for a specific machine learning problem domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kaixuan Liu, Guojun Xiong, Shengpu Tang, Wanyun Si, Jian Li ·

    Provably Efficient Representation Learning for Low-Rank CMDPs

    arXiv:2611.07295v1 Announce Type: new Abstract: We study representation learning in low-rank Constrained Markov Decision Processes (CMDPs), where both value and reward functions are approximated by a set of unknown representation vectors, and the transition dynamics admit a low-r…

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