reinforcement learning

Semantic Memory-Regularized UAMetaRL for Adaptive Task Scheduling in 6G Edge Intelligence

This repository provides the source code, configuration files, synthetic datasets, and experimental results for the paper entitled "Semantic Memory-Regularized Uncertainty-Aware Meta-Reinforcement Learning for Adaptive Task Scheduling in 6G Edge Intelligence". The repository implements a semantic memory-regularized unc...

KODEX — KDRIVE: RL-control

KODEX — KDRIVE (RL-control). reinforcement-learning controller — a feedback policy learned by the cross-entropy method in the twin plant loop; clamped by KGATE Benchmark: RL policy (cross-entropy): tracking **0.0551** vs naive 0.1225, twin-in-the-loop Part of KODEX, the Kronos Family of Codes — a benchmarked AI/ML fusi...

A reinforcement learning-guided hyper-heuristic algorithm for cold chain distribution planning

AI-DRIVEN ADMISSION SYSTEMS AND THEIR IMPACT ON ADMISSION INTAKE AND INSTITUTIONAL DIVERSITY: A REVIEW

With AI quickly making its way into higher education, many scholars are reevaluating how it decides the admissions. A systematic review of the peer-reviewed literature published during 2019 and 2025 throws light to the state of knowledge in relation to AI-led admission systems the details of operational architectures a...

Hybrid Reinforcement Approach for Customized Learning and Path Suggestion

A student can learn anywhere in the world by the unique chance created by E-learning and its advantages. Because of network information technology development, the MOOCs platform has become very popular. At the same time, students get confused in choosing the correct course which fits for the career, creating an increa...

Intelligent Cloud Resource Allocation Using Reinforcement Learn-ing: Evaluating Accuracy, Adaptability, and Efficiency

Elastic clouds must continuously decide how much compute, memory and storage capacity to grant to workloads whose intensity and mix change from minute to minute, and classical threshold, queueing and forecasting controllers struggle to hold that balance without either over-provisioning or violating service level agreem...

Transformer-enhanced reinforcement learning for magnetorheological semi-active suspension control under false data injection attacks

This paper investigates the control of a semi-active suspension equipped with a magnetorheological (MR) damper under nonlinear hysteresis, hard physical constraints, and unknown false data injection (FDI) attacks. A nonlinear-hysteretic dynamic model that accounts for FDI attacks is established for a semi-active suspen...

Reinforcement-Learning–Based Collaborative Optimization of Load Regulation and Opportunistic Maintenance of Wind Turbines

Abstract. To address the difficulty in coordinating component-degradation regulation strategies with opportunistic maintenance timings over the full lifecycle of wind turbines, this study proposes a reinforcement-learning–based collaborative optimization method for wind-turbine load regulation and opportunistic mainten...

Coordinated Day-Ahead and Intra-Day Secure Dispatch of Renewable-Rich Power Systems Based on Unified Diffusion Scenario Generation and Deep Reinforcement Learning

The Cognitive Neuroscience of Category Learning

Category learning is an adaptive skill that enables organisms to efficiently interpret and respond to their environment. Evidence supports the existence of a declarative learning system that applies explicit rules and test hypotheses, and a procedural learning system that gradually forms stimulus-response associations...

KODEX — KDRIVE: RL-control

KODEX — KDRIVE (RL-control). reinforcement-learning controller — a feedback policy learned by the cross-entropy method in the twin plant loop; clamped by KGATE Benchmark: RL policy (cross-entropy): tracking **0.0551** vs naive 0.1225, twin-in-the-loop Part of KODEX, the Kronos Family of Codes — a benchmarked AI/ML fusi...

KODEX — KDRIVE: RL-control

KODEX — KDRIVE (RL-control). reinforcement-learning controller — a feedback policy learned by the cross-entropy method in the twin plant loop; clamped by KGATEBenchmark: RL policy (cross-entropy): tracking **0.0551** vs naive 0.1225, twin-in-the-loopPart of KODEX, the Kronos Family of Codes — a benchmarked AI/ML fusion...