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reinforcement learning

2,609 papers

#reinforcement learning Dataset Open access Sep 2026

Literature Synthesis Matrix

A comprehensive extraction grid covering the 21 selected primary studies. Synthesizes structural data including author, publication venue, process mining domain, reinforcement learning approach, and explainability (XAI) focus.

Uday Nadgauda · 0 citations
#reinforcement learning Dataset Open access Sep 2026

Literature Synthesis Matrix

A comprehensive extraction grid covering the 21 selected primary studies. Synthesizes structural data including author, publication venue, process mining domain, reinforcement learning approach, and explainability (XAI) focus.

Uday Nadgauda · 0 citations
#reinforcement learning Review Open access Sep 2026

A Cross-Layer Review of Intelligent, Secure, and Privacy-Preserving Internet of Vehicles

This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions.

Mohanad Alayedi, Ahmad M. Jaradat · 0 citations
#reinforcement learning Dataset Open access Sep 2026

Regime-Aware Reinforcement Learning for Inflation Forecast Combination in Sub-Saharan Africa

This repository contains the complete data, code, trained models, intermediate outputs, robustness checks, and figures supporting the manuscript “Regime-Aware Reinforcement Learning for Inflation Forecast Combination in Sub-Saharan Africa.” The empirical dataset covers eight Sub-Saharan African economies—Côte d’Ivoire,...

REINDOLPH OSEI-ANIM · 0 citations
#reinforcement learning Open access Sep 2026

The Effectiveness of Instagram Reels and Flashcards for Vocabulary Learning: A Mixed-Methods Study with Sri Lankan ESL Undergraduates

This mixed-methods study examined the effectiveness of Instagram Reels and traditional flashcards as vocabulary learning interventions for teaching vocabulary to first-year English as a Second Language (ESL) undergraduates at a Sri Lankan state university. Grounded in Cognitive Load Theory, Dual Coding Theory, and Mult...

P. B. S. L. Pushpakumara, K. S. P. Dias, H. M. H. N. M. Jayawardana · 0 citations
#reinforcement learning Open access Sep 2026

A hierarchical navigation decision-making method for UAV swarms in unknown communication-constrained environments

In recent years, Unmanned Aerial Vehicles (UAVs) have gradually been widely used in various fields such as regional search and disaster relief, and the development of related technologies has also experienced unprecedented growth. Compared to individual UAVs, the collaborative execution of tasks by UAV swarms has more...

Hua-Jie Xiong, Bao-Guo Yu, Jing-Kui Zhang et al. · 0 citations
#reinforcement learning Open access Sep 2026

MA-RL and WOA-Based Secure Routing: A Federated Learning Approach for Trust Evaluation and DoS Attack Mitigation in IoT Networks

The growing size and sophistication of the Internet of Things (IoT) has brought up the issue of network security and routing efficiency, especially in unfriendly settings where Denial-of-Service (DoS) attacks are frtaent. Conventional trust-based routing algorithms are not very flexible and are not capable of adequatel...

M. N. Karuppusamy, N. Sasirekha · 0 citations
#reinforcement learning Open access Sep 2026

ANALISIS UPAYA GURU DALAM MENGEMBANGKAN KEPERCAYAAN DIRI PESERTA DIDIK DI KELAS III SD NEGERI 075048 LASARA SIWALUBANUA

Low student self-confidence in rural schools frequently hinders active participation, yet empirical studies addressing everyday teacher-led classroom interventions in these settings remain scarce. This study aims to analyze teachers’ efforts in developing the self-confidence of third-grade students at SD Negeri 075048...

Sepni Putri Indartin Waruwu, Otilina Gulo, E. Zai et al. · 0 citations
#reinforcement learning Open access Sep 2026

Observational Aliasing Bounds Generalization in Constrained Active-Sensing Multi-Agent Reinforcement Learning

Code, grading instruments, run provenance and a claim-to-source traceability register for the IEEE ICET 2026 paper of the same title. All four authors share the affiliation: School of Computing Sciences, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, Pakistan.

Muhammad Umair Waseem, Abdullah, Shayan Khan et al. · 0 citations
#reinforcement learning Open access Sep 2026

eta-ctrl

A modular framework for rolling-horizon operational control using a Gym-compatible agent–environment abstraction. The framework enables integration of optimization-based agents (Pyomo), reinforcement learning agents (Stable-Baselines3), heuristic search methods, and rule-based controllers. Environments can be instantia...

Andreas Clément, Jerome Stock, Julius Nicholas Balzer et al. · 0 citations
#reinforcement learning Dataset Open access Sep 2026

Regime-Aware Reinforcement Learning for Inflation Forecast Combination in Sub-Saharan Africa

This repository contains the complete data, code, trained models, intermediate outputs, robustness checks, and figures supporting the manuscript “Regime-Aware Reinforcement Learning for Inflation Forecast Combination in Sub-Saharan Africa.” The empirical dataset covers eight Sub-Saharan African economies—Côte d’Ivoire,...

REINDOLPH OSEI-ANIM · 0 citations

From tech blogs

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Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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