Results 11 to 20 of about 241,763 (280)

Constrained Deep Q-Learning Gradually Approaching Ordinary Q-Learning [PDF]

Frontiers in Neurorobotics, 2019
A deep Q network (DQN) (Mnih et al., 2013) is an extension of Q learning, which is a typical deep reinforcement learning method. In DQN, a Q function expresses all action values under all states, and it is approximated using a convolutional neural ...
Shota Ohnishi, Eiji Uchibe, Yotaro Yamaguchi, Kosuke Nakanishi, Yuji Yasui, Shin Ishii, Shin Ishii   +6 more
doaj   +3 more sources

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   +3 more sources

Using reinforcement learning in genome assembly: in-depth analysis of a Q-learning assembler [PDF]

Frontiers in Bioinformatics
Genome assembly remains an unsolved problem, and de novo strategies (i.e., those run without a reference) are relevant but computationally complex tasks in genomics.
Kleber Padovani, Rafael Cabral Borges, Roberto Xavier, André Carlos Carvalho, Anna Reali, Annie Chateau, Ronnie Alves, Ronnie Alves   +7 more
doaj   +2 more sources

Q-learning with censored data [PDF]

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   +6 more sources

Reducing the Prevalence of Coronavirus (COVID-19) in Airlines Based on and the Reinforcement Artificial Intelligence [PDF]

فناوری در مهندسی هوافضا, 2022
This paper proposes a method based on the artificial intelligence reinforcement Q-learning algorithm and paired comparison technique to solve the problem of health monitoring devices shortage in airlines.
Iman Shafieenejad, Mahyar Sadeghi, Mohammmad Siami Araghi, Sharareh Ghasemi   +3 more
doaj   +1 more source

Generalized Speedy Q-Learning [PDF]

IEEE Control Systems Letters, 2020
In this paper, we derive a generalization of the Speedy Q-learning (SQL) algorithm that was proposed in the Reinforcement Learning (RL) literature to handle slow convergence of Watkins' Q-learning. In most RL algorithms such as Q-learning, the Bellman equation and the Bellman operator play an important role.
Indu John, Chandramouli Kamanchi, Shalabh Bhatnagar   +2 more
openaire   +2 more sources

Reinforcement learning using Deep $$Q$$ Q networks and $$Q$$ Q learning accurately localizes brain tumors on MRI with very small training sets

BMC Medical Imaging, 2022
Background Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition.
J. N. Stember, H. Shalu
doaj   +1 more source

Reinforcement Learning-Based Routing Protocols in Vehicular and Flying Ad Hoc Networks – A Literature Survey

Promet (Zagreb), 2022
Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs).
Pavle Bugarčić, Nenad Jevtić, Marija Malnar   +2 more
doaj   +1 more source

QSPCA: A two-stage efficient power control approach in D2D communication for 5G networks

Intelligent and Converged Networks, 2021
The existing literature on device-to-device (D2D) architecture suffers from a dearth of analysis under imperfect channel conditions. There is a need for rigorous analyses on the policy improvement and evaluation of network performance. Accordingly, a two-
Saurabh Chandra, Prateek, Rohit Sharma, Rajeev Arya, Korhan Cengiz   +4 more
doaj   +1 more source

Cooperative Output Regulation By Q-learning For Discrete Multi-agent Systems In Finite-time

Journal of Applied Science and Engineering, 2022
This article studies the output regulation of discrete-time multi-agent systems with an unknown model by a finite-time optimal control algorithm based on Q-learning that uses the method of the linear quadratic regulator (LQR).
Wenjun Wei, Jingyuan Tang
doaj   +1 more source