reinforcement learning

Applications of Artificial Intelligence in Industry 4.0/5.0

This Reprint presents recent advances in Artificial Intelligence (AI) and its applications within the evolving paradigms of Industry 4.0 and Industry 5.0. It brings together contributions addressing intelligent manufacturing, machine learning, computer vision, reinforcement learning, industrial automation, predictive a...

Emergent Behavioral Consistency in a PersistentAutonomous Agent without Behavioral Prompts or Constraint-Based Safety Mechanisms.

UPDATE : 5. Codebase Verification: Zero Behavioral Instructions5.1 Keyword SearchA central claim of this work is that the surrounding architecture contains no behavioral instructions. This claim isverifiable. The following search was conducted across all five Python source files:Search patterns: 'you must', 'you should...

Value-Aware Estimation of Diffusion Systems for Continuous-Time Reinforcement Learning: Surrogate Loss and Financial Applications

Abstract. We estimate diffusion systems modeled by SDEs to solve optimal control problems in a continuous-time model-based reinforcement learning framework. Instead of minimizing a statistical loss, we estimate models by reducing the mismatch between the model-based value process and empirical rewards under a given pol...

Cross-attention multi-scale graph reinforcement learning for dynamic flexible job-shop scheduling under hybrid uncertainty

Intelligent Wireless EV Charging for Green Transportation: A Deep Reinforcement Learning Approach with Multi-Stage Current Battery Management

Electric vehicle (EV) wireless power transfer (WPT) systems face two fundamental challenges that hinder their widespread adoption: sensitivity to coil misalignment and the need for battery-friendly fast charging protocols. This paper presents a comprehensive and integrated framework that addresses both challenges throu...

Sample-Efficient Model-Based Reinforcement Learning for Autonomous Droplet Navigation via Controller Initialization

Model-based reinforcement learning (MBRL) is well suited to controlling physical systems that resist analytical modeling, but its reliance on real-world interaction makes data collection the dominant cost. This paper quantifies the effect of initialization choice on this cost, on a newly adapted robotic platform that g...

Research on Deep Reinforcement Learning-Driven Music Melody Generation Algorithms

Deploying artificial intelligence for occupational and environmental health in waste management: a systematic review

Objectives This systematic review examines how artificial intelligence (AI) mitigates occupational and environmental health risks in waste management, a sector where workers and communities face infectious, toxic, and physical hazards. Methods Following PRISMA guidelines, we systematically searched the SCOPUS database...

Codebase for Reinforcement Learning for Quantum Network Control with Application-Driven Objectives

A reinforcement learning (RL) framework written in C++ as a building block for quantum network control. The code implements the MDPs from the work titled "Reinforcement Learning for Quantum Network Control with Application-Driven Objectives," using policy gradients and the chain rule to optimize arbitrary non-linear ob...

Auditors, Not Oracles: Sparse Human Grounding for Bias-Aware Self-Alignment

Reinforcement learning from human feedback (RLHF) treats annotators as preference oracles. This is expensive and can encode annotator-pool bias into the reward signal: rater demographics, positional artifacts, and misspecified helpfulness criteria can become part of what the model is trained to produce. A growing body...

Lane-free signal control: simulator, controllers and result files

Code and result files behind a manuscript under anonymous review on signalcontrol for lane-free mixed traffic seen by a roadside camera. The archive contains a lane-free heterogeneous microscopic simulator with ajunction box, a pedestrian phase and a height-aware roadside camera thatpairs every camera frame with the tr...

Review on Modeling, Simulation, and Intelligent Control of Anaerobic Digestion for Food Waste Management

Anaerobic digestion (AD) is a promising technology for the valorization of food waste into renewable energy and nutrient-rich digestate while reducing greenhouse gas emissions. This review focus on the evolution of analysis models from mechanistic models like ADM1 and its extensions to data-driven and hybrid approaches...