Home › Intelligence › AI AI Nov 12, 2026 1 min read Published by arXiv AI · Nov 12, 2026 · 6:10 PM UTCOn GETSECURE.ai · Nov 13, 2026 · 12:20 PM UTC Introducing Feedback Delays to Non-Delayed Adversarial MDPs: Optimal $\sqrt S$ and $\sqrt K$ Dependencies in Tabular and Linear AMDPs Title card · GETSECURE.ai We study regret minimization in episodic Adversarial Markov Decision Processes (AMDPs) with unknown transitions and bandit feedback.Excerpt from arXiv AIGETSECURE.ai hasn't published its own summary of this story yet. The complete report is at arXiv AI:Read the full story at arXiv AI → References arXiv AI. (2026, November 12). Introducing Feedback Delays to Non-Delayed Adversarial MDPs: Optimal $\sqrt S$ and $\sqrt K$ Dependencies in Tabular and Linear AMDPs. arXiv AI. https://arxiv.org/abs/2611.17214v1 Original Source arXiv AI Read original → Related Intelligence AIOptimal last-iterate convergence in matrix games with bandit feedback using the log-barrierApr 17, 2026AIIntroducing Gemini 4 HydrogenNov 12, 2026AIIntroducing ChatGPT WalletNov 13, 2026AIIntroducing Our Smallest, Most Efficient Model: Muse EmberNov 10, 2026 Get the Signal. Skip the Noise. Weekly intelligence briefing — curated, scored, explained. Subscribe
