arXiv AI June 30, 2026 By Rajib Mostakim, Reza T. Batley, Sourav Saha

Agile Reinforcement Learning through Separable Neural Architecture and Applications

arXiv:2601. 23225v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet go-to function approximators - multilayer perceptrons (MLPs) - are often parameter-inefficient due to an imperfect inductive bias for the smooth structure of many value functions.

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

arXiv Machine Learning

Aug 4

An Embedded RISC-V Evaluation of Kolmogorov--Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models

arXiv:2608. 00737v1 Announce Type: new Abstract: Hard-constrained recurrent physics-informed networks (HRPINNs) embed known dynamics inside a recurrent numerical integrator and restrict a neural branch to learning only the residual dynamics that the first-principles model does not capture.

By Enzo Nicolas Spotorno, Josafat Leal Filho

efficiency

arXiv Machine Learning

Jun 25

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

arXiv:2606. 26002v1 Announce Type: new Abstract: We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks.

By Kamar Hibatallah Baghdadi, Kawther Guoual Belhamidi, Sara Belhadj, Aissa Boulmerka, Nadir Farhi

llms agents computer-vision reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 14

Constrained Reinforcement Learning for Safe Heat Pump Control

arXiv:2409. 19716v2 Announce Type: replace-cross Abstract: Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and performance across diverse control tasks.

By Baohe Zhang, Lilli Frison, Thomas Brox, Joschka B\"odecker

reinforcement-learning benchmarks safety

arXiv AI

Aug 6

Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality

arXiv:2510. 17709v2 Announce Type: replace-cross Abstract: Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive.

By Akhil S Anand, Shambhuraj Sawant, Paavo Parmas, Jasper Hoffmann, Dirk Reinhardt, Sebastien Gros

reinforcement-learning robotics

arXiv AI

Jun 2

Interpretable Policy Distillation for Power Grid Topology Control

arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.

By Aleksandra Dmitruka, Karlis Freivalds

agents reinforcement-learning efficiency

arXiv AI

Jun 9

An Agency-Transferring Model-Free Policy Enhancement Technique

arXiv:2606. 09825v1 Announce Type: cross Abstract: Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation.

By Anton Bolychev, Georgiy Malaniya, Sinan Ibrahim, Pavel Osinenko

rag agents reinforcement-learning benchmarks

Related stories

An Embedded RISC-V Evaluation of Kolmogorov--Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

Constrained Reinforcement Learning for Safe Heat Pump Control

Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality

Interpretable Policy Distillation for Power Grid Topology Control

An Agency-Transferring Model-Free Policy Enhancement Technique