reinforcement learning

Radial-residual energy-aware deep learning framework for sustainable cloud–IoT intelligence in smart cities

Smart city Internet of Things (IoT) networks is generating a continuous stream of heterogeneous sensor data that tends to require a timely analysis under the strict energy, latency, and computational constraints. Existing cloud-edge learning approaches have improved IoT intelligence, but they often treat feature learni...

A fault-aware, explainable proximal policy optimization based intelligent control framework with neuromorphic-inspired encoding for voltage and frequency stability

Abstract High penetration of inverter-interfaced generation, faults, and sudden load changes in power systems makes them more vulnerable to voltage and frequency deviations. Traditional controllers often struggle to adapt to varying disturbance types and severities in real time. Adaptive, data-driven control methods ar...

ProphetRouterRL: code, simulation setup and results

Code, simulation setup and results accompanying the paper "ProphetRouterRL: A Reinforcement Learning Enhanced Routing Protocol for V2X Delay Tolerant Networks" (submitted to MDPI Network). ProphetRouterRL keeps PRoPHET's delivery-predictability comparison as a first filter and adds a small tabular Q-learning agent that...

Risk-aware hierarchical reinforcement learning for investment and operations in cross-border energy supply chains under the belt and road initiative

Cross-border energy supply chains under the Belt and Road Initiative (BRI) face unprecedented challenges, including geopolitical instability, macroeconomic volatility, and operational disruptions. Traditional optimization or flat reinforcement learning (RL) methods often struggle to address the scale, uncertainty, an...

ProphetRouterRL: code, simulation setup and results

Code, simulation setup and results accompanying the paper "ProphetRouterRL: A Reinforcement Learning Enhanced Routing Protocol for V2X Delay Tolerant Networks" (submitted to MDPI Network). ProphetRouterRL keeps PRoPHET's delivery-predictability comparison as a first filter and adds a small tabular Q-learning agent that...

ANALISIS INTEGRASI PENDIDIKAN KARAKTER SISWA KELAS IV DALAM PEMBELAJARAN SENI BUDAYA DAN PRAKARYA DI SD NEGERI 90 PALEMBANG

ABSTRACT Character education needs to be integrated into learning so that positive values are not merely understood conceptually but also practiced in students’ behavior. This study aims to describe the integration of character education among fourth-grade students in Arts, Culture, and Crafts (SBdP) learning at SD Neg...

LLM-Driven Verilog Generation and Verification for RISC-V Processor Design

Large language models (LLMs) are increasingly used in Electronic Design Automation (EDA) to write hardware description code. This paper reviews how LLMs generate and verify Verilog for a RISC-V processor datapath, a core topic in Computer Architecture and Organization. The review is built around the AI-driven logic syn...

Code and Data for: Non-linear and probabilistic state discretization functions for enhanced discrete reinforcement learning: application on wind turbine pitch control

This repository contains the supplemental research artifacts for the manuscript titled 'Non-linear and probabilistic state discretization functions for enhanced discrete reinforcement learning: application on wind turbine pitch control.' The provided materials ensure the reproducibility of the proposed discretization m...

Reinforcement learning-driven inverse design of programmable resonant acoustic metamaterials

Artificial Intelligence in Electronic Design Automation: A Survey on Logic Synthesis and Netlist Optimization

Modern digital integrated circuits contain very large numbers of logic gates, and the conventional heuristics used in logic synthesis and netlist optimization struggle to explore the resulting design space efficiently. Artificial Intelligence (AI) and Machine Learning (ML) have therefore been investigated as complement...

Project TALOS: Tactical Agentic Literature Orchestration System

Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.

Dynamic Difficulty Adjustment in Video Games: A Review of Approaches, Applications and Open Challenges

This paper is a narrative literature review examining Dynamic Difficulty Adjustment (DDA) in video games. It covers the psychological foundations of DDA (Flow Theory and Self-Determination Theory), the main technical approaches used to implement it including rule-based methods, player modeling, reinforcement learning,...