Results 61 to 70 of about 199,848 (147)

Adaptive Q-Learning Grey Wolf Optimizer for UAV Path Planning

Drones
Path planning is crucial for safely and efficiently navigating unmanned aerial vehicles (UAVs) toward operational goals. Often, this is a complex, multi-constraint, and non-linear optimization problem, and metaheuristic algorithms are frequently used to ...
Golam Moktader Nayeem, Mingyu Fan, Golam Moktader Daiyan   +2 more
doaj   +1 more source

Q-learning with censored data

The Annals of Statistics, 2012
We develop methodology for a multistage-decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages.
Goldberg, Yair, Kosorok, Michael R.
openaire   +4 more sources

Maximum Power Point Tracking of Photovoltaic System Based on Reinforcement Learning

Sensors, 2019
The maximum power point tracking (MPPT) technique is often used in photovoltaic (PV) systems to extract the maximum power in various environmental conditions.
Kuan-Yu Chou, Shu-Ting Yang, Yon-Ping Chen   +2 more
doaj   +1 more source

Deep Q-learning From Demonstrations

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor.
Hester, Todd, Vecerik, Matej, Pietquin, Olivier, Lanctot, Marc, Schaul, Tom, Piot, Bilal, Horgan, Dan, Quan, John, Sendonaris, Andrew, Dulac-Arnold, Gabriel, Osband, Ian, Agapiou, John, Leibo, Joel Z., Gruslys, Audrunas   +13 more
openaire   +2 more sources

CUBIC-Learn: A Reinforcement Learning Approach to CUBIC Congestion Control

Jordanian Journal of Computers and Information Technology
Managing congestion effectively enables reliable and fast data transfer over networks. CUBIC delivers reliable results under normal circumstances but cannot adapt effectively to changing network scenarios.
Ehsan Abedini, Mohsen Nickray
doaj   +1 more source

A Reinforcement Learning Approach to Solve Service Restoration and Load Management Simultaneously for Distribution Networks

IEEE Access, 2019
Energy and economy are increasing the relationship over the years, where the energy becomes a significant resource to keep a country developing, and it supports its economy.
Lucas Roberto Ferreira, Alexandre Rasi Aoki, Germano Lambert-Torres   +2 more
doaj   +1 more source

Maxmin Q-learning: Controlling the Estimation Bias of Q-learning

, 2020
ICLR ...
Lan, Qingfeng, Pan, Yangchen, Fyshe, Alona, White, Martha   +3 more
openaire   +2 more sources

Adaptive Q-learning

, 2013
Developing an effective multi-stage treatment strategy over time is one of the essential goals of modern medical research. Developing statistical inference, including constructing confidence intervals for parameters, is of key interest in studies applying dynamic treatment regimens.
Goldberg, Yair, Song, Rui, Kosorok, Michael R.   +2 more
openaire   +2 more sources

Una estrategia híbrida de aprendizaje por refuerzo informada por RRT* para la planificación de caminos de robots móviles en minería a cielo abierto

Revista Iberoamericana de Automática e Informática Industrial RIAI
Este trabajo introduce una estrategia híbrida de planificación de caminos para vehículos robóticos tipo diferencial, combinando métodos de aprendizaje por refuerzo con técnicas de muestreo aleatorio.
Sebastian Zapata, Ricardo Urvina, Katherine Aro, Eduardo Aguilar, Fernando Auat Cheein, Alvaro Prado   +5 more
doaj   +1 more source

Q-learning Based Meta-Heuristics for Scheduling Bi-Objective Surgery Problems with Setup Time

Complex System Modeling and Simulation
Since the increasing demand for surgeries in hospitals, the surgery scheduling problems have attracted extensive attention. This study focuses on solving a surgery scheduling problem with setup time. First, a mathematical model is created to minimize the
Ruixue Zhang, Hui Yu, Adam Slowik, Kaizhou Gao   +3 more
doaj   +1 more source