reinforcement learning

An interpretable deep reinforcement learning framework for dynamic wind farm maintenance scheduling with enhanced power generation and stability

Aversive Memory and Extinction Learning for Noxious Stimuli and Aversive Tastants in Bumblebees

The data are from a conditioning experiment testing how bumblebees learn and retain associations between flower colours and aversive stimuli (electric shock or salt), including how avoidance behaviour is extinguished over repeated foraging bouts in the absence of reinforcement.

Energy Optimized Green Light Assist in Varying Traffic Scenarios Using Reinforcement Learning

Optimizing traffic flow at signalized intersections is a central challenge in road networks. Existing approaches address this problem through measures such as adaptive traffic signal control to decrease travel time and reduce vehicle energy consumption. A complementary approach is the optimization of vehicle-level driv...

Edge-Cloud Intelligent Monitoring and Maintenance for Smart Production Lines

INTRODUCTION: Smart manufacturing production lines integrate Computer Numerical Control (CNC) machines, industrial robots, motor drives, conveying units, sensors, and Programmable Logic Controller (PLC) workstations. Their operation and maintenance (O&M) requires low-latency recognition of coupled vibration, temperatur...

Mechanism-driven reinforcement learning for ultrafiltration scheduling: From event triggering to proactive cycle orchestration

Ultrafiltration systems are significantly affected by dynamic evolution of membrane fouling during long-term operation, exhibiting highly nonlinear operational behaviors and intricate control coupling. To overcome the constraints encountered by conventional control strategies in industrial applications, including respo...

Learning from Sequences: A Review of Machine Learning and Deep Learning for Text, Speech, Behavioural, Financial and Genomic Data

Much of the data that matter in computing arrive as sequences: the words a user types, the sounds of a language, the purchases in a basket, the daily prices of a stock and the bases of a genome. In each case, the meaning of an element depends on the elements around it. Methods for such data have moved from frequency co...

Reinforcement-learning co-adaptation analysis of thruster-based wearable robotic assistance for running training in musculoskeletal simulation

Hybrid Residual Reinforcement Learning Control of a Fuel Cell Converter

The MATLAB/Simulink models, controller scripts, multi-seed metric workbooks, and figure-generation scripts that support the findings of this study are available here.

Does AI-based building energy optimisation deliver net energy savings? Code, hardware energy-measurement logs and results

Code, per-job hardware energy-measurement logs, classical-controller results, derived tables and figure scripts for the study "Does AI-based building energy optimisation deliver net energy savings? A measured life-cycle assessment of computational energy against operational benefits across 2,000+ building–climate–algor...

Adaptive Multi-Objective Deep Reinforcement Learning for Policy-Driven Neural Architecture Search of Resource-Efficient CNNs

Neural Architecture Search (NAS) automates the process of designing neural networks but the focus is mostly on prediction accuracy without considering the model complexity and computational cost. This research study proposes a new framework called Adaptive Multi-Objective Policy-Driven Neural Architecture Search (AMO-P...

Structure-Preserving Reinforcement Learning for Language Models: Leveraging Geometry

We propose a geometric framework for structure-preserving reinforcement learning in the fine-tuning of large language models (LLMs), grounded in Lie algebraic geometry. The approach systematically identifies geometric constraints in representation spaces by analyzing semantic transformations and detecting cases where e...

Control of Error, Not a Gun to the Head: A Montessori-Informed Lesson Plan for Reinforcement-Learning Harness Design

This methods note proposes a Montessori-informed approach to reinforcement-learning (RL) evaluation-harness design, centered on Maria Montessori's principle of control of error. The paper examines the engineering consequences of incorrect answer keys, ambiguous evaluation items, and faulty reward signals. Its central p...