reinforcement learning

Deep reinforcement learning-based adaptive FOPID tuning for power system stability enhancement: A twin delayed deep deterministic policy gradient approach

Abstract This paper proposes a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based adaptive fractional-order proportional-integral-derivative (FOPID) controller for joint load-frequency and voltage regulation in a renewable-rich power system. The proposed controller adaptively tunes five FOPID parameters, namel...

Process-Model-Supported Offline Reinforcement Learning for Water–Fertilizer Management: Two-Model Closed-Loop Production–Nitrogen-Loss Trade-Offs

Nitrogen leaching threatens groundwater quality, making production retention and efficient water–fertilizer use joint management priorities. Field trials evaluate a limited set of treatments, while adaptive policies generate long sequences of decisions. We propose counterfactual rollout-constrained offline reinforcemen...

Calibration-Aware Reinforcement Learning for Large Language Models: A Survey of Objectives, Optimization, and Decision-Making

Large language models increasingly emit confidence reports, predictive distributions, and typed decisions that determine whether a system answers, abstains, retrieves evidence, or spends more computation. We survey calibration-aware reinforcement learning (RL), in which a reported probability is scored by the reward, c...

Multi-Objective Optimization of an Integrated Bio-Hydrogen Liquefaction System Using Surrogate-Assisted NSGA-II and Multi-Agent Reinforcement Learning

Reinforcement Learning for Optimizing Automated Logic Gates

Abstract The optimization of logic gates plays a crucial role in Electronic Design Automation (EDA), typically managed by applying deterministic, rule-based heuristics to And-Inverter Graph (AIG) models of a circuit. As circuit complexity rises to billions of gates, these heuristic methods find it increasingly difficul...

THE EL-RAKHAWI DOCTRINE ON NEURO-ADAPTIVE AI AND THE PROHIBITION OF COGNITIVE EXPLOITATION Law, Computational Neuroscience, and the Defense of Free Will in the Age of Closed-Loop Interfaces

The El-Rakhawi Doctrine on Neuro-Adaptive AI and the Prohibition of Cognitive Exploitation by Dr. M. K. A. El-Rakhawi presents the first legal framework defending human free will against Closed-Loop Neuro-Adaptive Systems (CL-NAS). These systems use Reinforcement Learning to exploit neural vulnerabilities and hijack de...

THE EL-RAKHAWI DOCTRINE ON NEURO-ADAPTIVE AI AND THE PROHIBITION OF COGNITIVE EXPLOITATION Law, Computational Neuroscience, and the Defense of Free Will in the Age of Closed-Loop Interfaces

The El-Rakhawi Doctrine on Neuro-Adaptive AI and the Prohibition of Cognitive Exploitation by Dr. M. K. A. El-Rakhawi presents the first legal framework defending human free will against Closed-Loop Neuro-Adaptive Systems (CL-NAS). These systems use Reinforcement Learning to exploit neural vulnerabilities and hijack de...

What Does the Reward Pay For? An Empirical Study of Reward Hacking and Reward Design in Reinforcement Learning for Unit Test Generation

AQ-OLSR: ADAPTIVE ROUTING PROTOCOL FOR UAV AD HOC NETWORK Merged with DEEP Q-NETWORK (DQN) ALGORITHM

Unmanned Aerial Vehicles (UAVs) have emerged recently due to rapid improvements in wireless technology and low-cost equipment, advancement in networking communication techniques, and increased demand from various industries that seek to leverage aerial data to improve their business and operations. As such, UAVs have b...

Artificial Intelligence in Environmental Monitoring, Pollution Control, and Low-Carbon Management

Air pollution, water contamination, soil degradation, solid waste accumulation, and carbon emissions are increasingly interconnected, posing common challenges to environmental engineering, including diverse monitoring targets, heterogeneous data sources, competing control objectives, and delayed management responses. T...

Data and Code Supporting “Physics-Guided Surrogate Learning Enables Zero-Shot Control of Turbulent Wings”

This dataset supports the study “Physics-guided surrogate learning enables zero-shot control of turbulent wings.” The research investigates whether control policies trained in computationally tractable turbulent channel flows can be transferred directly, without additional training, to the turbulent boundary layer over...

AI-Powered Logic Gate Optimization for Efficient Electronic Design

Abstract The continuous development of modern electronic devices has resulted in increasingly complex Integrated Circuits (ICs). As the number of logic gates and circuit components increases, designing efficient electronic systems becomes more challenging. Engineers need to consider several important factors, including...