arXiv Machine Learning July 7, 2026 By Kyohei Suzuki, onstantinos Slavakis

Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions

Read the original on arXiv Machine Learning →

arXiv:2607. 04990v1 Announce Type: new Abstract: This work delivers two key contributions: one to efficient feature selection in reinforcement learning (RL), the other to the theory of non-monotone inclusions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Oct 3

Rate-Optimal Algorithm for Adversarial Linear CMDPs

The paper introduces a new primal–dual algorithm for episodic adversarial linear constrained Markov decision processes (CMDPs) with unknown transitions. It achieves a rate‑optimal ×O(√K) regret and cumulative constraint violation, improving upon the previous ×O(K^{3/4}) bound and eliminating the need for Slater’s condition. The method combines adaptive FTRL, contracted value estimation, and an exponential Lyapunov function, enabling uniform concentration over the value function class and computational efficiency independent of the state‑space size.

By Kihyun Yu, Honghao Wei, Dabeen Lee

reinforcement-learning safety

arXiv AI

Oct 15

Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning

arXiv:2610.14988v1 Announce Type: cross Abstract: The average-reward setting for reinforcement learning is challenging because its Bellman operator is known to contract in span only when the underlyi...

By Julien Roger, Sam Bodmer, Jian Qian, Benjamin Riviere

reinforcement-learning safety

arXiv Machine Learning

Jun 11

Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity

arXiv:2606. 11431v1 Announce Type: new Abstract: Mirror Descent (MD) extends Gradient Descent (GD) beyond Euclidean geometry and has recently reappeared as a lens for KL-regularized policy optimization in reinforcement learning and LLM post-training.

By Shira Vansover-Hager, Matan Schliserman, Ofir Schlisselberg, Tomer Koren

llms reinforcement-learning

arXiv Machine Learning

Jun 9

A Robust $\widetilde{\mathcal{O}}(1/\sqrt{T})$ Rate for Unprojected TD Learning with Linear Function Approximation

arXiv:2506. 01052v3 Announce Type: replace Abstract: We investigate the finite-time convergence properties of Temporal Difference (TD) learning with linear function approximation, a cornerstone of reinforcement learning.

By Wei-Cheng Lee, Francesco Orabona

reinforcement-learning

arXiv Statistics ML

Sep 10

Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions

arXiv:2603.15576v2 Announce Type: replace-cross Abstract: This paper develops new variance-reduction techniques for the forward-reflected-backward splitting (FRBS) method to solve a class of possibly...

By Quoc Tran-Dinh, Nghia Nguyen-Trung

reinforcement-learning

arXiv Machine Learning

Jul 9

Mathematical methods of reinforcement learning

arXiv:2607. 06935v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly grounded in tools from probability, optimization, and operator theory.

By Denis Belomestny, Alexander Gasnikov, Egor Gladin, Alexey Naumov, Artemy Rubtsov, Yuri Sapronov, Daniil Tiapkin, Nikita Yudin

reinforcement-learning

One email a morning, machine-written

Related stories

Rate-Optimal Algorithm for Adversarial Linear CMDPs

Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning

Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity

A Robust $\widetilde{\mathcal{O}}(1/\sqrt{T})$ Rate for Unprojected TD Learning with Linear Function Approximation

Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions

Mathematical methods of reinforcement learning