arXiv Machine Learning July 21, 2026 By Xinyu Dai, Daniel Chen, Yian Qian

Dynamic Decision-Making under Model Misspecification: A Stochastic Stability Approach

Read the original on arXiv Machine Learning →

arXiv:2602. 17086v2 Announce Type: replace-cross Abstract: Dynamic decision-making under model uncertainty is central to many economic environments, yet existing bandit and reinforcement learning algorithms rely on the assumption of correct model specification.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

reinforcement-learning

arXiv Machine Learning

Jun 2

Finite-Time Regret Analysis of Retry-Aware Bandits

arXiv:2605. 20854v2 Announce Type: replace Abstract: We study a stochastic bandit algorithm motivated by retry-aware objectives that value the best outcome among multiple attempts, such as pass@$k$ and max@$k$.

By Bingkui Tong, Junpei Komiyama, Soichiro Nishimori, Paavo Parmas

reinforcement-learning

arXiv Machine Learning

Jun 24

Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits

arXiv:2606. 23933v1 Announce Type: cross Abstract: We study non-stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes systematically biased.

By AmirHossein Naghdi, Ali Baheri

reinforcement-learning benchmarks

arXiv AI

Aug 12

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

arXiv:2605. 23146v3 Announce Type: replace-cross Abstract: Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy.

By Manish Aryal, Faiyaz Azam, Agnivo Banerjee, Syed Mahir Ahamed, Sai Sidhanth Manoharan Jayanthi, Allegra Laro, Cl\'ement Legentilhomme, Andrew Lin, Florian Lorkowski, Marina P\'erez del Valle, Radman Rakhshandehroo, Patric Rommel, Emanuel Ruzak, Nathan Theng, Paul Yushin Rapoport

agents reinforcement-learning safety

arXiv Machine Learning

Aug 4

Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability

arXiv:2512. 09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty.

By Simone Cuonzo, Nina Deliu

reinforcement-learning

arXiv Machine Learning

Jun 2

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

arXiv:2604. 08149v2 Announce Type: replace Abstract: We consider a linear contextual bandit model where contexts and rewards are governed by a finite hidden Markov chain.

By Zhen Li (LMO, CELESTE, HEC Paris), Gilles Stoltz (LMO, CELESTE, HEC Paris)

reinforcement-learning

arXiv Machine Learning

Jun 24

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces

arXiv:2603. 08287v2 Announce Type: replace-cross Abstract: We analyze the Bayesian regret of the Gaussian process posterior sampling reinforcement learning (GP-PSRL) algorithm.

By Hamish Flynn, Joe Watson, Ingmar Posner, Jan Peters

reinforcement-learning

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Finite-Time Regret Analysis of Retry-Aware Bandits

Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces