arXiv Machine Learning

Near-Minimax Sample Complexity for Multi-Objective Reinforcement Learning under Generative Models

arXiv Machine Learning

Sep 30

Optimal Multi-Reward Reinforcement Learning

arXiv:2609.36486v1 Announce Type: new Abstract: We study an unknown-transition finite-horizon Markov decision process (MDP) with a finite collection of known reward functions $\{r^1, r^2, \ldots, r^M...

By Zijun Chen, Zihan Zhang

reinforcement-learning

arXiv Machine Learning

Jun 25

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.

By Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet

agents reinforcement-learning

arXiv Machine Learning

Aug 10

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

arXiv:2608. 06545v1 Announce Type: new Abstract: Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty.

By Yuepeng Yang, Yuxin Chen, Yuejie Chi

reinforcement-learning safety

arXiv Machine Learning

Aug 18

Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs

arXiv:2509. 16586v2 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDPs) under the generative model.

By Yukuan Wei, Xudong Li, Lin F. Yang

reinforcement-learning safety

arXiv Machine Learning

Sep 22

Optimal Sample Complexity of Stable Discounted Markov Decision Processes

arXiv:2302.07477v4 Announce Type: replace Abstract: We study the optimal sample complexity of tabular reinforcement learning for infinite-horizon discounted Markov decision processes. The unrestricte...

By Shengbo Wang, Jose Blanchet, Peter Glynn

reinforcement-learning

arXiv Machine Learning

Jul 23

Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.

By Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du

reinforcement-learning

arXiv Machine Learning

Oct 12

Near-Minimax-Optimal Rates for Asynchronous TD Learning with Markovian Data

arXiv:2609. 38880v2 Announce Type: replace-cross Abstract: We study the last iterate of standard importance-weighted temporal-difference (TD) learning from a single trajectory of a finite Markov decision process.

By Yang Peng

reinforcement-learning

arXiv Statistics ML

Oct 27

Minimax Regret for Sobolev Kernel-Based Reinforcement Learning

arXiv:2610.34045v1 Announce Type: cross Abstract: We study the minimax regret rate for episodic kernel-based reinforcement learning, where the expected reward and transition density belong to a Sobol...

By Haichen Hu, David Simchi-Levi

reinforcement-learning safety

arXiv Machine Learning

Oct 12

Hitting-Time Bounds for Asynchronous Q-Learning

arXiv:2610. 13901v1 Announce Type: cross Abstract: We study the last iterate of standard asynchronous Q-learning from a single trajectory of a finite Markov decision process.

By Yang Peng

reinforcement-learning

arXiv Machine Learning

Sep 17

A Convergence Framework for Deep $V$-Learning: Error Propagation and Sharp Action-Gap Bounds

The paper presents a convergence framework for deep $V$‑learning over a finite horizon $H$, deriving explicit bounds on policy loss by decomposing the Bellman update error into six residuals. It shows how $L^s$ concentrability controls expected $L^1$ loss, quantifies the impact of shared sampling across horizon levels, and provides optimal and near‑optimal sample allocations for statistical error rates. The work also establishes sharp action‑gap bounds under a margin condition, transfers optimal‑gap results to frozen‑iterate gaps, and offers consistency guarantees for generative‑reset approximate‑ERM procedures with exact action scores.

By Yury Kolomeytsev

arXiv Machine Learning

Aug 27

Minimax Alternating Regret for the Experts Problem and Online Convex Optimization

arXiv:2608. 25182v1 Announce Type: cross Abstract: In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of alternating learning dynamics in two-player games.

By Mengxiao Zhang

Hugging Face Trending Papers

Sep 30

Sharp Statistical Rates for Asynchronous TD Learning with Markovian Data

We study the last iterate of standard tabular temporal-difference (TD) learning from a single trajectory of a finite Markov reward process. For discount factor $γ$, write $H=(1-γ)^{-1}$, and let $μ_{\...

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