reinforcement learning

Regime-Aware Reinforcement Learning: A Mixture-of-Experts Framework for Dynamic Asset Allocation

Recent advances in reinforcement learning (RL) have spurred growing interest in its application to multi-period financial planning. Existing literature broadly follows three paradigms: hybrid RL, scalable RL, and end-to-end RL. This paper develops a hybrid regime-aware RL framework for dynamic asset allocation that emb...

Reproduction code and screened corpus for a survey of evaluation design in reinforcement learning route guidance

Reproduction material for the survey "Study-Area Boundaries in Reinforcement Learning-Based Route Guidance: A Survey of Evaluation Design and ReportingRequirements" by Donghoun Lee. The archive contains the grid, benchmark and SUMO environments, the planner, the training code, the screened corpus withthe recorded field...

Controllable molecular generation with fine-tuned flow-matching model

A flexible reinforcement learning method is introduced for flow-matching based generative models, allowing the velocity field to be refined according to a user-defined reward function, enabling dynamic fine-tuning and joint optimization of features, outperforming baseline approaches in diverse optimization scenarios.

PERAN LEMBAGA BIMBINGAN BELAJAR PRIVAT ASSALAM DALAM MENUMBUHKAN KEPERCAYAAN DIRI BELAJAR SISWA SEKOLAH DASAR

ABSTRACT Students’ learning confidence at the elementary school level does not always develop through academic learning alone, particularly when they remain hesitant to ask questions, answer questions, express their opinions, or complete tasks without assistance. This condition provided the basis for examining the role...

TOPO-RLHF: The First Integration of Topological Permanence with Reinforcement Learning from Human Feedback

TOPO-RLHF: Full Summary Core Contribution TOPO-RLHF presents the first integration of topological permanence with Reinforcement Learning from Human Feedback (RLHF), enabling existing models to acquire new capabilities without forgetting prior knowledge. The Problem It Solves Catastrophic Forgetting (1989-2026) AI syste...

TOPO-RLHF: The First Integration of Topological Permanence with Reinforcement Learning from Human Feedback

TOPO-RLHF: Full Summary Core Contribution TOPO-RLHF presents the first integration of topological permanence with Reinforcement Learning from Human Feedback (RLHF), enabling existing models to acquire new capabilities without forgetting prior knowledge. The Problem It Solves Catastrophic Forgetting (1989-2026) AI syste...

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...

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...