reinforcement learning

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...

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...

CSPPO: A multi-agent reinforcement learning method for production capacity allocation in manufacturing industry chains

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.

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...

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,...

An Intelligent Distributed Adaptive Control Method for Multi-Channel Thermal Regulation in Fire-Resistance Testing Equipment

Accurate regulation of combustion temperature is critical for objectively evaluating the fire-resistance performance of cables. However, existing temperature control strategies mainly rely on centralized regulation methods, which struggle to simultaneously address the nonlinear coupling among multiple heat sources, spa...

Physics-Constrained Multi-Agent Reinforcement Learning for Coordinated Multi-Bed Direct Air Capture under Dynamic Weather and Shared Utility Constraints: Reproducibility Package

Definitive reproducibility package for a four-bed TVSA direct-air-capture MARL study using Lewatit VP OC 1065 and real TMYx 2011–2025 weather. The archive contains the frozen process/control code, processed weather inputs, checkpoints, a complete eight-seed nominal benchmark for Fixed, Rule, RollingHorizon, CentralPPO,...

ST-SafeMAPPO: code, processed NREL ARPA-E PERFORM / OPSD test systems and simulation configurations for minute-level power system production simulation with deep reinforcement learning and safety constraints

Reference implementation, processed test systems and simulation configurations accompanying the manuscript Minute-Level Power System Production Simulation Algorithm Based on Deep Reinforcement Learning and Safety Constraints (Zheng Li, Juan Su, Yuhao Fu, Yue Xia). The archive contains: (1) the Python source code of ST-...

Code for "Reinforcement learning discovers new mechanisms of reentry in excitable media"

Code associated with the manuscript “Reinforcement learning discovers new mechanisms of reentry in excitable media.” This archive corresponds to the current manuscript submission and includes the cellular automaton simulations, reinforcement-learning environments and training scripts, policy evaluation, simulations in...

A Combined Framework for Agile Story Point Estimation Using AHP-Based Expert Weighting, Neutrosophic Z-Numbers, Bonferroni Aggregation, and XGBoost

Accurate story point estimation is crucial for effective agile planning in software development. Both primary methods have inherent limitations. Expert judgment is susceptible to subjectivity, whereas machine learning models require extensive, high-quality, project-specific datasets that are often unavailable. This stu...

Systematic Study Exclusion Reasons

Itemized breakdown documenting the explicit technical reasons for study exclusion during the full-text screening process (e.g., absence of reinforcement learning components or reliance on gray literature).