IEEE

Reliability-Aware Placement of Virtual Network Functions via Multi-Agent Deep Reinforcement Learning

Abstract: Network Function Virtualization (NFV) often suffers from unexpected failures in some mission-critical applications due to the reliability-agnostic deployment of Virtual Network Functions ...
Scientific Research Publishing

Vanishing Gradients in an Artificial Deep Neural Network: The Impact of Activation Functions and Optimization Techniques ()

Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is the ...
IEEE

Multi-Objective Deep Reinforcement Learning Driven Collaborative Optimization of TSV-Based Microchannel and PDN for 3D ICs

Abstract: This study introduces a multi-objective deep reinforcement learning (MODRL) framework for the concurrent thermal-hydraulic optimization of the through-silicon via (TSV) microchannel heat ...
C&EN

Bio-Digital Catalyst Design: Generative Deep Learning for Multi-Objective Optimization and Chemical Insights in CO

School of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei 230009, China Anhui Province Key Laboratory of Value-Added Catalytic Conversion and Reaction Engineering, Hefei ...
Frontiers

Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization

A reward shaping deep deterministic policy gradient (RS-DDPG) and simultaneous localization and mapping (SLAM) path tracking algorithm is proposed to address the issues of low accuracy and poor ...