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Analysing Deep Reinforcement Learning Agents Trained with Domain
  Randomisation

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation

18 December 2019
Tianhong Dai
Kai Arulkumaran
Tamara Gerbert
Samyakh Tukra
Feryal M. P. Behbahani
Anil Anthony Bharath
ArXivPDFHTML

Papers citing "Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation"

26 / 76 papers shown
Title
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
232
19,796
0
07 Oct 2016
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous
  Off-Policy Updates
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
S. Gu
E. Holly
Timothy Lillicrap
Sergey Levine
OffRL
SSL
109
1,477
0
03 Oct 2016
Target-driven Visual Navigation in Indoor Scenes using Deep
  Reinforcement Learning
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
Yuke Zhu
Roozbeh Mottaghi
Eric Kolve
Joseph J. Lim
Abhinav Gupta
Li Fei-Fei
Ali Farhadi
VGen
57
1,516
0
16 Sep 2016
Pruning Filters for Efficient ConvNets
Pruning Filters for Efficient ConvNets
Hao Li
Asim Kadav
Igor Durdanovic
H. Samet
H. Graf
3DPC
175
3,687
0
31 Aug 2016
OpenAI Gym
OpenAI Gym
Greg Brockman
Vicki Cheung
Ludwig Pettersson
Jonas Schneider
John Schulman
Jie Tang
Wojciech Zaremba
OffRL
ODL
186
5,056
0
05 Jun 2016
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning
  and Large-Scale Data Collection
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Sergey Levine
P. Pastor
A. Krizhevsky
Deirdre Quillen
148
2,071
0
07 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
746
16,828
0
16 Feb 2016
Graying the black box: Understanding DQNs
Graying the black box: Understanding DQNs
Tom Zahavy
Nir Ben-Zrihem
Shie Mannor
65
263
0
08 Feb 2016
Asynchronous Methods for Deep Reinforcement Learning
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih
Adria Puigdomenech Badia
M. Berk Mirza
Alex Graves
Timothy Lillicrap
Tim Harley
David Silver
Koray Kavukcuoglu
168
8,805
0
04 Feb 2016
Learning Deep Features for Discriminative Localization
Learning Deep Features for Discriminative Localization
Bolei Zhou
A. Khosla
Àgata Lapedriza
A. Oliva
Antonio Torralba
SSL
SSeg
FAtt
200
9,280
0
14 Dec 2015
Learning both Weights and Connections for Efficient Neural Networks
Learning both Weights and Connections for Efficient Neural Networks
Song Han
Jeff Pool
J. Tran
W. Dally
CVBM
269
6,628
0
08 Jun 2015
High-Dimensional Continuous Control Using Generalized Advantage
  Estimation
High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman
Philipp Moritz
Sergey Levine
Michael I. Jordan
Pieter Abbeel
OffRL
60
3,368
0
08 Jun 2015
Understanding deep features with computer-generated imagery
Understanding deep features with computer-generated imagery
Mathieu Aubry
Bryan C. Russell
54
148
0
03 Jun 2015
End-to-End Training of Deep Visuomotor Policies
End-to-End Training of Deep Visuomotor Policies
Sergey Levine
Chelsea Finn
Trevor Darrell
Pieter Abbeel
BDL
249
3,418
0
02 Apr 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
219
18,534
0
06 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.1K
149,474
0
22 Dec 2014
Striving for Simplicity: The All Convolutional Net
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
198
4,653
0
21 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
147
3,261
0
05 Dec 2014
Understanding Deep Image Representations by Inverting Them
Understanding Deep Image Representations by Inverting Them
Aravindh Mahendran
Andrea Vedaldi
FAtt
99
1,959
0
26 Nov 2014
Exact solutions to the nonlinear dynamics of learning in deep linear
  neural networks
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe
James L. McClelland
Surya Ganguli
ODL
138
1,830
0
20 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
214
7,252
0
20 Dec 2013
Network In Network
Network In Network
Min Lin
Qiang Chen
Shuicheng Yan
245
6,267
0
16 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
389
15,825
0
12 Nov 2013
Rich feature hierarchies for accurate object detection and semantic
  segmentation
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross B. Girshick
Jeff Donahue
Trevor Darrell
Jitendra Malik
ObjD
233
26,122
0
11 Nov 2013
DeCAF: A Deep Convolutional Activation Feature for Generic Visual
  Recognition
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue
Yangqing Jia
Oriol Vinyals
Judy Hoffman
Ning Zhang
Eric Tzeng
Trevor Darrell
VLM
ObjD
172
4,946
0
06 Oct 2013
On the difficulty of training Recurrent Neural Networks
On the difficulty of training Recurrent Neural Networks
Razvan Pascanu
Tomas Mikolov
Yoshua Bengio
ODL
159
5,318
0
21 Nov 2012
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