reinforcement learning

Optimal sizing and deep reinforcement learning-based control of a battery energy storage system under renewable and load uncertainty in an isolated microgrid

Uncertainty-aware reinforcement learning for joint optimization of data center operations and electricity market bidding

AI-Enabled Performance-Based Procurement and Life-Cycle Maintenance of Highway Bridges: Integrating Single-Bid Risk Analytics and PPP Payment Optimization

Model-based life-cycle evaluation indicates that AI-optimized PPP contracts reduce bridges reaching emergency condition by 30%–40% over a 30-year horizon while lowering life-cycle costs by 8%–12% compared with rule-based policies, providing infrastructure agencies and private concessionaires with an integrated AI-drive...

Sample-Efficient Robot Skill Learning for Construction Tasks: Benchmarking Hierarchical Reinforcement Learning and Vision-Language-Action Model

Overall, the findings indicate that VLA offers practical advantages for changing tasks by reducing programming effort and enabling useful performance with minimal data, while DQN provides a viable baseline when sufficient tuning effort is acceptable.

Physics-Guided Reinforcement Learning for the multidimensional optimization of MXene-modified photoanodes in photoelectrochemical water splitting

Intrusion detection system in IIoT using Scottish red deer optimization assisted deep reinforcement learning model

Deep reinforcement learning for autonomous and continual network intrusion detection

Physical PPG signal synthesis via reinforcement learning-based LED control

Deep reinforcement learning for dynamic multi-trip multi-AGV scheduling in product retrieval systems

A multi-agent deep reinforcement learning framework for coordinated urban traffic signal and parking lot exit control

A hybrid physics-aware multi-objective analog integrated circuit design framework with deep reinforcement learning

Physics-Informed Hybrid Machine Learning Model for Carbonation Depth Prediction in Concrete through Residual Correction and Variogram Analysis of Response Surfaces

A hybrid residual correction framework that integrates a physics-based carbonation model with a stacked ensemble of machine learning algorithms: gradient boosted regression trees (GBRT), support vector regression (SVR), and Gaussian process regression (GPR), combined through an XGBoost metamodel, demonstrating that the...