reinforcement learning

STRATEGI GURU DALAM MENGELOLA KECEMASAN BERBAHASA SISWA KELAS VI PADA MAHARAH KALAM BAHASA ARAB

Arabic language learning as a foreign language is frequently hindered by foreign language anxiety, thereby affecting students' mastery of maharah kalam (speaking skills). However, if students continuously experience such anxiety, the objective of speaking instruction to enhance oral competence in Arabic will be difficu...

EOL-DQN: An intelligent cutting decision of potato seed tubers based on improved deep reinforcement learning

An explainable AI framework for crack width and crack spacing prediction with serviceability-oriented design optimization of reinforced and prestressed concrete members

Reliable prediction of cracking behavior is essential for the serviceability assessment of reinforced and prestressed concrete members, where crack development depends on interacting material, geometric, reinforcement, prestressing, and loading parameters. This study presents an explainable and uncertainty-aware artifi...

Trans-SAC: gamification design for sustained user engagement in residential demand response

Abstract Residential Demand Response (DR) is a critical mechanism for maintaining the supply-demand balance in modern smart grids. While gamification has recently emerged as a promising strategy to incentivize residential participation, existing programs suffer from severe long-term user fatigue. Once the initial "nove...

Data and code for "Humans and large language models rely on distinct strategies for adaptive decision-making under uncertainty"

human vs LLM in adaptive decision making, including four reinforcement learning tasks(one-armed, two-armed bandit tasks, reversal learning task and two-step task)

Moving target defense in 5G and beyond networks: A comprehensive survey and research directions

This paper presents a deployment-oriented survey of MTD for 5G, with special emphasis on learning-assisted designs that optimize what to mutate, when to mutate, and how to enforce mutation under slice-isolation, URLLC, and orchestration constraints.

ARISE: Reinforcement learning for dynamic critical-node intervention in rumor-epidemic co-evolution

A knowledge-guided hierarchical multi-agent deep reinforcement learning approach for crowd evacuation

Dynamic Topological Dependency Graph Learning (DTTL)

This paper introduces Dynamic Topological Dependency Graph Learning (DTTL), a novel approach to program dependency analysis that leverages reinforcement learning to automatically construct and refine dependency graphs without manual intervention. Traditional static dependency analysis tools are limited in their ability...

基于多智能体协作的复杂系统模拟

This paper presents a novel approach to simulating complex systems by leveraging the power of multi-agent collaboration. The core idea is to construct a population of intelligent agents, each with varying levels of cognitive ability, and task them with solving complex problems collectively. Reinforcement learning is em...

Dynamic Resource Allocation in Serverless Architectures via Reinforcement Learning

Serverless computing architectures offer significant advantages in terms of scalability and cost-efficiency. However, traditional serverless resource allocation strategies often rely on static configurations or simple heuristics, which can lead to suboptimal performance and increased costs. This paper proposes a novel...

Development of a Collective Intelligence Framework for Optimising Demand and Supply-Side Smart Energy Management to Support Net-Zero Energy Outcomes in the Built Environment

Achieving net-zero energy in residential precincts remains challenging due to diverse energy demand patterns, intermittent renewable energy generation, and the need to balance energy demand, supply, thermal comfort, and affordability. Existing studies have focused on optimising demand and supply to reduce grid energy c...