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New research tackles optimal regret in adversarial MDPs · 2 papers

By PulseAugur Editorial · [2 sources] ·

Two new research papers submitted to arXiv explore advancements in adversarial Markov decision processes (MDPs). The first paper introduces MA-OPS, an algorithm designed to achieve optimal regret while adhering to stochastic hard constraints, improving upon existing theoretical bounds. The second paper focuses on policy optimization within adversarial MDPs, proposing a method using regularized Q-functions to close the horizon gap and achieve improved regret bounds compared to previous algorithms. AI

IMPACT Advances theoretical understanding and algorithmic approaches for sequential decision-making under uncertainty in adversarial environments.

RANK_REASON Two academic papers published on arXiv detailing new algorithms and theoretical bounds for adversarial Markov decision processes.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research tackles optimal regret in adversarial MDPs · 2 papers

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Qian Zuo, Francesco Emanuele Stradi ·

    Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints

    arXiv:2610.12153v1 Announce Type: new Abstract: We study episodic constrained Markov decision processes with adversarial losses under stochastic hard constraints. Specifically, starting from a known strictly feasible policy with margin $d$, we seek to obtain optimal regret while …

  2. arXiv cs.LG TIER_1 English(EN) · Mingyi Li, Taira Tsuchiya ·

    Closing the Horizon Gap in Policy Optimization for Adversarial MDPs

    arXiv:2610.12362v1 Announce Type: new Abstract: We consider policy optimization for online episodic tabular Markov decision processes (MDPs) with adversarial losses and bandit feedback. Policy optimization updates the policy locally at each state and avoids optimization over the …

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