reinforcement learning

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).

Verification Is Not Validity: From Self-Verifiable Rewards to Governed Execution

A control-plane analysis distinguishing three concepts routinely collapsed in contemporary AI discourse: event verifiability, proxy validity, and governance sufficiency. Taking Reinforcement Learning with Self-Verifiable Rewards and its SpyRL instantiation as its case, it accepts the engineering contribution — an envir...

Machine learning-based framework for real-time seismic damage assessment of RC buildings in Nepal

Seismic fragility curves help assess structural damage based on a few structural and ground motion (GM) features. In Nepal, diverse construction practices exist, and current standards require reinforced concrete (RC) buildings to ensure structural integrity under design spectra for different soil types. To study this...

Reinforcement-learning-based adaptive energy management strategy for microgrid energy storage systems

High renewable penetration makes microgrid energy management sensitive to uncertain photovoltaic output, wind fluctuation, load variation, electricity price, and battery degradation. Conventional rule-based and model predictive strategies require manually tuned thresholds or accurate forecasts, which limits their adapt...

Recent Advances of Reinforcement Learning Algorithms for Autonomous Driving System

This survey examines RL-based AD in modular and end-to-end pipelines and relates reported methods to task formulation and deployment evidence and examines deployment barriers, including safety, Sim2Real generalization, data efficiency, computation, embodied alignment, and evaluation readiness.

arjyadebsengupta-netizen/Julia-based-SciML-and-Reinforcement-Learning-Applications: Julia Based ML and RL

Full Changelog: https://github.com/arjyadebsengupta-netizen/Julia-based-SciML-and-Reinforcement-Learning-Applications/commits/v.0.0

Geometry-guided and interaction-aware multi-agent reinforcement learning for cooperative multi-USV encirclement

MineAgent: Deep Reinforcement Learning-based Testing for Minecraft

This package contains the extended test results for my Bachelor thesis MineAgent: Deep Reinforcement Learning-based Testing for Minecraft at the University of Trento, supervised by Fondazione Bruno Kessler (FBK) researchers.