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New algorithm achieves optimal regret for adversarial linear CMDPs

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Researchers have developed a new primal-dual algorithm that achieves optimal $\widetilde{\mathcal{O}}(\sqrt{K})$ regret and cumulative constraint violation for episodic adversarial linear CMDPs with unknown transitions. This new algorithm overcomes the limitations of previous methods, which were bound by $\widetilde{\mathcal{O}}(K^{3/4})$. The approach combines adaptive Follow the Regularized Leader (FTRL), contracted value estimation, and an exponential Lyapunov function, eliminating the need for policy mixing and ensuring policy parameters remain compatible with uniform concentration. AI

RANK_REASON The cluster contains a research paper detailing a new algorithm for a specific type of decision-making process. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New algorithm achieves optimal regret for adversarial linear CMDPs

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The cluster contains a research paper detailing a new algorithm for a specific type of decision-making process. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kihyun Yu, Honghao Wei, Dabeen Lee ·

    Rate-Optimal Algorithm for Adversarial Linear CMDPs

    arXiv:2610.00927v1 Announce Type: new Abstract: We study episodic adversarial linear constrained Markov decision processes (CMDPs) with unknown transitions, where both the loss and constraint functions may vary adversarially across episodes. The best previous algorithm achieves $…

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