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NADPEx: An on-policy temporally consistent exploration method for deep
  reinforcement learning

NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning

21 December 2018
Sirui Xie
Junning Huang
Lanxin Lei
Chunxiao Liu
Zheng Ma
Wayne Zhang
Liang Lin
ArXivPDFHTML

Papers citing "NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning"

3 / 3 papers shown
Title
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning
Siddharth Aravindan
Dixant Mittal
Wee Sun Lee
BDL
79
0
0
17 Jan 2025
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,638
0
03 Jul 2012
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