reinforcement learning

Reinforcement Learning for the 6th Generation in Advanced Communications

Hybrid modeling and energy optimization of NGL demethanizers using deep reinforcement learning

Abstract Cryogenic NGL demethanizers suffer from energy inefficiencies due to non-convex thermodynamic constraints. This study aims to implement a Deep Reinforcement Learning (DRL) framework to optimize energy consumption while respecting complex operational boundaries. A Soft Actor-Critic (SAC) agent was employed, tra...

Learning-Assisted Energy Optimization of Electric Vehicle Fleets in Renewable-Rich Microgrids

In this paper, we propose a learning-enabled framework to support the energy management of EV fleets operating in renewable-rich microgrids. The framework leverages a combination of Reinforcement Learning, Model Predictive Control, and Probabilistic Renewable Forecasting to optimize the charging schedules of EVs and th...

AI Driven Jamming in 6G Networks

This paper investigates AI-driven jamming as an emerging security threat to sixth-generation (6G) wireless networks. Unlike conventional jamming techniques that rely on relatively fixed and predictable interference patterns, AI-driven jamming can use machine learning to sense network conditions, adapt attack strategies...

Marwa-Chouhdary/MEC_IIoT_E_DDPG_LS_MCOR_TD3_Code: MEC IIoT E-DDPG-LS and MCOR-TD3 Code v1.0.0

MCOR-TD3 for MEC-Enabled IIoT — v1.0.1 This release contains Python code associated with the paper "Adaptive Task Caching and Resource Allocation in MEC-Enabled IIoT Using Deep Reinforcement Learning." It supports simulation of cooperative task offloading and computing-resource allocation using MCOR-TD3 to minimize max...

Causal-Graph-Guided Reinforcement Learning for LLM Security Testing: An Empirical Study

This preprint investigates whether structural causal information derived from adversarial interaction trajectories can improve reinforcement-learning-based black-box security testing of large language models. The study combines PPO-based adaptive prompt search with an interpretable six-factor representation derived fro...

Machine Learning-Driven Strength Prediction of Intermediate Crack-Induced Debonding in FRP-Strengthened RC Beams

A Multi-Layer Learning Architecture for Optimal Power Sharing Between Electric Vehicles and Microgrids

A multi layer learning architecture is proposed for optimizing the distribution of electric vehicle power among microgrids. The architecture includes three layers: prediction, coordination, and real-time control. Using a prediction layer to forecast demand loads, available renewable energy generation rates, and vehicle...

Learning Together and Against Each Other: How Multiple Reinforcement-Learning Agents Coordinate, Compete and Where the Field is Heading

The obstacles that distinguish MARL from its singleagent counterpart are discussed, namely a moving-target learning problem, the difficulty of dividing a shared reward among team members, limited local views, and growth of the joint action space, and why training with global information but acting on local information...

Multibody dynamic modelling and guided reinforcement learning for a novel air suspension system of semi-trailers

Vehicle vibrations during long-haul logistics transportation can significantly degrade ride comfort and increase the risk of cargo damage. To address this issue, this paper proposes a novel torsion-beam air suspension configuration and a guided reinforcement learning–based semi-active control strategy. First, a multibo...

Corporate AI Training Program Template

A template for corporate AI training with role groups, learning objectives, governance practice, session design and post-training reinforcement.

Dynamic driver behavior modeling with GMM for sustainable traffic signal control in deep reinforcement learning

Adaptive traffic signal control is crucial for improving urban mobility and supporting environmental sustainability. Driver behavior heterogeneity has a significant impact on intersection performance, but its effective integration into reinforcement learning models remains challenging. This study proposes HARLight, a s...