Results 231 to 240 of about 298,347 (266)

Application of deep reinforcement learning for aerodynamic control around an angled airfoil via synthetic jet. [PDF]

Sci Rep
Hammouda NG, Khan R, Mostafa L, Musa VA, Rajab H, Singh NSS, Aich W, Hajlaoui K.   +7 more
europepmc   +1 more source

Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks. [PDF]

PLoS One
Haberland S, Ruge H, Frimmel H.
europepmc   +1 more source

Delay-Fluctuation-Resistant Underwater Acoustic Network Access Method Based on Deep Reinforcement Learning. [PDF]

Sensors (Basel)
Shi J, Tian K, Zhang J.
europepmc   +1 more source

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Deep Reinforcement Learning

2022
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas   +2 more
  +6 more sources

Reinforcement Learning and Deep Reinforcement Learning

2019
In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.
F. Richard Yu, Ying He
openaire   +1 more source

Deep Reinforcement Learning

2021
This chapter starts by covering the basic concepts involved in reinforcement learning and then describes how to solve reinforcement learning tasks by using basic and deep learning-based solutions. It also provides a brief overview of the typical algorithms central to the deep learning-based solutions, namely DQN, DDPG, and A3C.
openaire   +1 more source

Deep Reinforcement Learning: A Survey

IEEE Transactions on Neural Networks and Learning Systems
Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful end-to-end learning control capabilities. In the past decade, DRL has made substantial advances in many tasks that require perceiving high-dimensional input and ...
Xu Wang, Sen Wang, Xingxing Liang, Dawei Zhao, Jincai Huang, Xin Xu, Bin Dai, Qiguang Miao   +7 more
openaire   +2 more sources

Chimeric antigen receptor T‐cell therapy in multiple myeloma: A comprehensive review of current data and implications for clinical practice

Ca-A Cancer Journal for Clinicians, 2023

exaly  

Reinforcement and Deep Reinforcement Machine Learning

2017
Data-driven learning is a very strong concept. This concept is chased and converted into wonderful applications. Whole stream of Data Engineering and Data Sciences emerged out of that. The data is collected from various sources. It is collected from big hospitals, data repositories, from cookies running in your machines, intelligent applications ...
openaire   +1 more source