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New technique resolves open problems in Adversarial MDPs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a novel technique called "introduced feedback delays" to improve regret minimization in Adversarial Markov Decision Processes (AMDPs). This method, which involves estimating the Q-function using future episodes while still updating the policy, addresses challenges related to policy changes and data correlation. The proposed Geometric Resampling with Rejection (GRR) algorithm achieves optimal $\sqrt{S}$ and $\sqrt{K}$ dependencies in tabular and linear AMDPs, respectively, resolving long-standing open problems in the field. AI

IMPACT Introduces a novel algorithmic technique that could improve decision-making in complex, dynamic environments.

RANK_REASON Research paper detailing a new algorithmic technique for a specific class of Markov Decision Processes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

New technique resolves open problems in Adversarial MDPs

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Research paper detailing a new algorithmic technique for a specific class of Markov Decision Processes. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Introducing Feedback Delays to Non-Delayed Adversarial MDPs: Optimal $\sqrt S$ and $\sqrt K$ Dependencies in Tabular and Linear AMDPs

    We study regret minimization in episodic Adversarial Markov Decision Processes (AMDPs) with unknown transitions and bandit feedback. In tabular AMDPs with $S$ states, $A$ actions, and horizon $H$, the best known $K$-episode regret is $\tilde O(HS\sqrt{AK})$, whose $\sqrt S$ gap t…

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