reinforcement learning

Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications

This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...

GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability through Selective Answering

Current work on improving reliability in large language model (LLM)- generated answers has primarily leveraged Retrieval-Augmented Generation (RAG), knowledge-graph augmentation, and reinforcement learning. While these methods are adept at enhancing and measuring reliability through semantic similarity and faithfulness...

Reinforcement Learning for Cathode Material Design Through Sequential Decision-Making Frameworks

Reinforcement learning as a decision making technique for cathode material design has been evaluated and how RL can support the composition selection, dopant, and crystal structure optimisation is analyzed.

Run-to-Run Control of Chemical Mechanical Polishing Head With Adjustable Partition Pressure Based on Deep Reinforcement Learning

The polishing head is one of the most important components in the chemical mechanical polishing (CMP) process, used to achieve an ultra-smooth surface on the wafer. Due to the effect of the retaining ring and the replacement of the polishing pad, the polishing process is subjected to drift and shift interference. The C...

Data-driven bike-sharing rebalancing for smart cities: Reinforcement learning, operational constraints, and service-cost trade-offs

A risk-adaptive deep reinforcement learning approach for energy management of fuel cell hybrid electric vehicles

Enhancing PLS in UAV-Aided Backscatter Networks: A Multiagent DRL Framework with Federated Learning

In recent years, enhancing physical layer security (PLS) in UAV-aided backscatter networks has received huge attention among the research community due to its various applications, such as smart farming, cognitive healthcare, and intelligent industrial automation, among others. It results in secure and reliable data tr...

Research on asymmetric stochastic advertising differential games based on multi-agent deep reinforcement learning

Optimization of Underground Sublevel Stoping Layouts Considering Geological and Grade Uncertainty Using Deep Reinforcement Learning

From Power Concentration to Structured Light Generation: Scalable, Efficient and Generalizable Coherent Beam Combination With Model‐Based Reinforcement Learning

This work proposes a model‐based reinforcement learning (MBRL) framework that enables intelligent phase control by effectively emulating CBC system dynamics via a physics‐informed environment model, and establishes a powerful paradigm for integrating MBRL into high‐power optical field modulation.

Combating Web-Based E-Commerce Fraud Syndicates: Fairness-Aware Hypergraph Contrastive Fraud Detection with Multi-Dimensional Reinforcement Rewards

MRC, a novel semi-supervised model that integrates Multi-view Heterogeneous Hypergraph Contrastive learning (MHHC) and Multi-Dimensional Reinforcement reward driven Community detection (MDRC) for web-based e-commerce collusive fraud detection, is proposed.

Metanormative Theory for RL-Based Moral Agents

This paper aims to draw out ideas from recent work in metanormative theory that can be useful for designing artificial moral and value-aligned agents and examine the RL architecture through the lens of these ideas.