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The Predictron: End-To-End Learning and Planning

The Predictron: End-To-End Learning and Planning

28 December 2016
David Silver
H. V. Hasselt
Matteo Hessel
Tom Schaul
A. Guez
Tim Harley
Gabriel Dulac-Arnold
David P. Reichert
Neil C. Rabinowitz
André Barreto
T. Degris
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Papers citing "The Predictron: End-To-End Learning and Planning"

14 / 14 papers shown
Title
FACTS: A Factored State-Space Framework For World Modelling
FACTS: A Factored State-Space Framework For World Modelling
Li Nanbo
Firas Laakom
Yucheng Xu
Wenyi Wang
Jürgen Schmidhuber
AI4TS
490
1
0
28 Oct 2024
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
C. Voelcker
Marcel Hussing
Eric Eaton
Amir-massoud Farahmand
Igor Gilitschenski
82
4
0
11 Oct 2024
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a
  Survey
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey
Aske Plaat
W. Kosters
Mike Preuss
BDL
OffRL
94
17
0
11 Aug 2020
Recurrent Environment Simulators
Recurrent Environment Simulators
Silvia Chiappa
S. Racanière
Daan Wierstra
S. Mohamed
65
208
0
07 Apr 2017
Adaptive Computation Time for Recurrent Neural Networks
Adaptive Computation Time for Recurrent Neural Networks
Alex Graves
112
547
0
29 Mar 2016
Value Iteration Networks
Value Iteration Networks
Aviv Tamar
Yi Wu
G. Thomas
Sergey Levine
Pieter Abbeel
76
653
0
09 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
197
8,859
0
04 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,020
0
10 Dec 2015
On Learning to Think: Algorithmic Information Theory for Novel
  Combinations of Reinforcement Learning Controllers and Recurrent Neural World
  Models
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
Jürgen Schmidhuber
59
104
0
30 Nov 2015
Continuous control with deep reinforcement learning
Continuous control with deep reinforcement learning
Timothy Lillicrap
Jonathan J. Hunt
Alexander Pritzel
N. Heess
Tom Erez
Yuval Tassa
David Silver
Daan Wierstra
320
13,248
0
09 Sep 2015
Action-Conditional Video Prediction using Deep Networks in Atari Games
Action-Conditional Video Prediction using Deep Networks in Atari Games
Junhyuk Oh
Xiaoxiao Guo
Honglak Lee
Richard L. Lewis
Satinder Singh
103
853
0
31 Jul 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
463
43,305
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.8K
150,115
0
22 Dec 2014
Deeply-Supervised Nets
Deeply-Supervised Nets
Chen-Yu Lee
Saining Xie
Patrick W. Gallagher
Zhengyou Zhang
Zhuowen Tu
341
2,240
0
18 Sep 2014
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