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New bandit algorithm achieves logarithmic regret without eluder-dimension dependence

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new approach to KL-regularized contextual bandits, focusing on scenarios with both reward and preference feedback. Their work demonstrates that a simple greedy sampling method can achieve logarithmic regret without needing to depend on the eluder dimension. This method directly samples from the Gibbs policy derived from the estimated reward, and the analysis reveals a trade-off between greedy sampling and upper confidence bound-style exploration, depending on the strength of the KL regularization. AI

IMPACT This research could lead to more efficient reinforcement learning algorithms in various applications.

RANK_REASON The item is an academic paper detailing a new algorithm for contextual bandits. [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 bandit algorithm achieves logarithmic regret without eluder-dimension dependence

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The item is an academic paper detailing a new algorithm for contextual bandits. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zichen Wang, Haoyang Hong, Huazheng Wang ·

    When Greedy Sampling Explores: KL-Regularized Contextual Bandits without Eluder-Dimension Dependence

    arXiv:2609.13564v1 Announce Type: new Abstract: We study KL-regularized contextual bandits under both reward and preference feedback. We show that greedy sampling can achieve logarithmic regret without explicit dependence on the eluder dimension. For reward feedback, we establish…

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