Results 71 to 80 of about 199,848 (147)

Context Aware Task Orchestration With Deep Reinforcement Learning in Real Time Fog Computing Simulation Environment

IEEE Access
In the ever-evolving landscape of cloud computing, fog and edge computing have become more prominent because of their natural property of proximity to demanding parts.
Alp Gokhan Hossucu, Suat Ozdemir
doaj   +1 more source

Exploration design for Q-learning-based adaptive linear quadratic optimal regulators under stochastic disturbances

SICE Journal of Control, Measurement, and System Integration
This study considers a discrete-time, linear state feedback control strategy rooted in Q-learning, one of the Reinforcement Learning (RL) approaches, to address an adaptive Linear Quadratic (LQ) problem under stochastic disturbances. Q-learning optimizes
Vina Putri Virgiani, Shiro Masuda
doaj   +1 more source

A Navigation Algorithm Based on the Reinforcement Learning Reward System and Optimised with Genetic Algorithm

Mathematics
Regarding autonomous vehicle navigation, reinforcement learning is a technique that has demonstrated significant results. Nevertheless, it is a technique with a high number of parameters that need to be optimised without prior information, and correctly ...
Mireya Cabezas-Olivenza, Ekaitz Zulueta, Iker Azurmendi-Marquinez, Unai Fernandez-Gamiz, Danel Rico-Melgosa   +4 more
doaj   +1 more source

Flow Q-Learning

ICML ...
Park, Seohong, Li, Qiyang, Levine, Sergey   +2 more
openaire   +2 more sources

AHT-QCN: Adaptive Hunt Tuner Algorithm Optimized Q-learning Based Deep Convolutional Neural Network for the Penetration Testing

Cybernetics and Information Technologies
Penetration Testing (PT), which mimics actual cyber attacks, has become an essential procedure for assessing the security posture of network infrastructures in recent years.
Railkar Dipali, Joshi Shubhalaxmi
doaj   +1 more source

Empirical analysis of control models for different converter topologies from a statistical perspective

Science and Technology for Energy Transition
Converter topologies including SEPIC, ZETA, etc. are controlled via selection of capacitive and inductive components, which assists in improving its conversion efficiency for different type of loads.
Hole Shreyas Rajendra
doaj   +1 more source

Control of Dynamics Systems Based on Q-learning

Advances in Electrical and Electronic Engineering, 2004
The purpose of the paper is to present an algorithm for solving nonlinear systems control problem based on Q-learning,which is a model-free approach belonging to the adaptive critic family of designs and its advantage over other algorithms of thisfamily ...
Anna Filasova, Juraj Klacik, Jan Kasprisin   +2 more
doaj  

Spike-based Q-learning in a non-von Neumann architecture. [PDF]

Front Neurosci
Shin D, Jo H, Jang H, Jeong YH, Jeong Y, Kwak JY, Park J, Lee S, Kim I, Park JK, Park S, Jang HJ, Lee HM, Kim J.   +13 more
europepmc   +1 more source

Deep Q-Learning for Gastrointestinal Disease Detection and Classification. [PDF]

Bioengineering (Basel)
Saba A, Amin J, Ali MU.
europepmc   +1 more source

Blockchain and federated Q-learning-based secure, fault tolerant, and energy efficient framework for ad hoc networks. [PDF]

PLoS One
Tandon R, Gupta PK, Cheng X.
europepmc   +1 more source