Automated healthcare IoT systems demand secure, low-latency, and energy-efficient computation—capabilities well-supported by fog computing. Effective selection of fog nodes is critical for maximizing ...
This project implements the research paper "Reinforcement Learning for Risk-Aware Portfolio Optimization: A Comparative Study of PPO, QR-DDPG, DDPG, and SAC under Market Uncertainty".
Abstract: In continuous casting and rolling (CCR) systems, precise billet cutting is critical for ensuring product dimensional accuracy and minimizing material waste. However, conventional rule-based ...
Abstract: The increasing penetration of renewable energy sources and the integration of distributed energy resources (DERs) have significantly increased the stochastic nature of modern industrial ...
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Greenhouse vegetable production was a complex agricultural system influenced by multiple interrelated environmental and management factors. Its irrigation control was a critical but not singularly ...
ABSTRACT: This work presents a new method to improve the security of Internet of Things (IoT) networks using a model combining an autoencoder LSTM (AE-LSTM) and a centralized multi-agent reinforcement ...
A modular, cross-platform Proximal Policy Optimization (PPO) implementation that can be integrated into JavaScript SPAs, Node.js apps, Unity 3D games, Python applications, and more. The system uses a ...
Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.
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