reinforcement learning

Personalized Learning Pathways: A Review

In today&s;s rapidly evolving educational landscape, personalized learning recommendation systems (PLRS) have become essential for addressing diverse learner needs in an online environment. This review paper provides a comprehensive analysis of recent advancements in adaptive and personalized recommendation techniques...

Adaptive Cloud Workflow Scheduling via LSTM-Enhanced Deep Reinforcement Learning

Advanced Machine Learning Techniques for Crime Prediction in Smart Heterogeneous Policing Networks

The proposed chapter suggest a highly complex machine learning crime prediction model on an intelligent heterogeneous policing network, which will have to consider dynamic environment, multimodal, and real-time decision-making. A deep learning (CNN LSTM) hybrid architecture that is founded on graph neural networks and...

Adaptive optimization and interaction method for immersive exhibition space experiences integrating multimodal physiological sensing

Interactions in immersive exhibition halls rely heavily on explicit inputs such as controllers and touchscreens, making it difficult to respond in real time to users’ unconscious emotional fluctuations, which frequently disrupts immersion. This paper proposes an adaptive optimization and interaction method that integra...

Decomposed Q-Learning for Fixture Selection in Multi-resource-Constrained Flexible Job Shop Scheduling

Hyperparameter-Optimized machine learning approach for compressive strength prediction of metakaolin concrete reinforced with glass fiber

Dynamic defense strategies for cyber-physical systems using Stackelberg games and deep reinforcement learning in discrete and continuous time

This study presents a dual-domain defense framework that combines Stackelberg game theory with Deep Reinforcement Learning that offers a robust, adaptable toolset for CPS defense by clarifying the trade-offs between temporal abstractions in nonlinear and dynamic environments.

From Static to Dynamic: Revolutionizing Restaurant Pricing Strategies

The restaurant industry has a problem with the changing customer demand that it cannot match with its fixed operational capacity, leading to loss of revenue and inefficient use of resources. Traditional static pricing models are not capable of adjusting to real-time demand changes and other contextual factors, thus res...

Reinforcement learning for initializing genetic algorithms in vehicle routing

This work introduces an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions, which are then further optimized by a genetic algorithm, enabling real-time and interactive routing at scale.

Reward Shaping based on Trajectory Quality for offline and hybrid reinforcement learning

Current offline reinforcement learning algorithms typically apply equal learning strength to samples of different quality during training, which can limit the effective utilization of high-quality trajectories. To solve this problem, we propose Reward Shaping based on Trajectory Quality (RSTQ). RSTQ increases the rewar...

Data-Driven Adaptive Reactive Power and Voltage Control Method for Distribution Networks with Energy Storage by Using DRL-SAC Algorithm

In distribution network and microgrids, energy storage (ES) systems possess four-quadrant operational capabilities, making them inherently high-quality resources for reactive power (RP) regulation. However, existing research has primarily focused on optimizing the active power of ES to achieve economic objectives, whil...

Next-Generation AI-Driven Threat Detection in Smart and Connected Public Safety Networks

As the world of smart, connected public safety networks expands in scale and sophistication, it is increasingly susceptible to attacks combining cyber and physical capabilities. The chapter suggests a novel framework for threat detection based on AI, which involves the fusion of multimodal intelligence, graph neural re...