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Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative
  for Training Deep Neural Networks for Reinforcement Learning

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

18 December 2017
F. Such
Vashisht Madhavan
Edoardo Conti
Joel Lehman
Kenneth O. Stanley
Jeff Clune
ArXivPDFHTML

Papers citing "Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning"

26 / 276 papers shown
Title
VINE: An Open Source Interactive Data Visualization Tool for
  Neuroevolution
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
Rui Wang
Jeff Clune
Kenneth O. Stanley
11
7
0
03 May 2018
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Adam Gaier
A. Asteroth
Jean-Baptiste Mouret
SyDa
23
17
0
15 Apr 2018
Coevolutionary Neural Population Models
Coevolutionary Neural Population Models
N. Moran
Jordan Pollack
14
5
0
11 Apr 2018
Supervising Unsupervised Learning with Evolutionary Algorithm in Deep
  Neural Network
Supervising Unsupervised Learning with Evolutionary Algorithm in Deep Neural Network
Takeshi Inagaki
BDL
23
0
0
28 Mar 2018
Automated Curriculum Learning by Rewarding Temporally Rare Events
Automated Curriculum Learning by Rewarding Temporally Rare Events
Niels Justesen
S. Risi
OffRL
35
20
0
19 Mar 2018
Neural Network Quine
Neural Network Quine
Oscar Chang
Hod Lipson
18
23
0
15 Mar 2018
Policy Search in Continuous Action Domains: an Overview
Policy Search in Continuous Action Domains: an Overview
Olivier Sigaud
F. Stulp
16
72
0
13 Mar 2018
The Surprising Creativity of Digital Evolution: A Collection of
  Anecdotes from the Evolutionary Computation and Artificial Life Research
  Communities
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman
Jeff Clune
D. Misevic
C. Adami
L. Altenberg
...
Danesh Tarapore
S. Thibault
Westley Weimer
R. Watson
Jason Yosinksi
10
279
0
09 Mar 2018
Transfer Learning with Neural AutoML
Transfer Learning with Neural AutoML
Catherine Wong
N. Houlsby
Yifeng Lu
Andrea Gesmundo
19
114
0
07 Mar 2018
Model-Based Stochastic Search for Large Scale Optimization of
  Multi-Agent UAV Swarms
Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms
David D. Fan
Evangelos Theodorou
J. Reeder
29
14
0
03 Mar 2018
Autostacker: A Compositional Evolutionary Learning System
Autostacker: A Compositional Evolutionary Learning System
Boyuan Chen
Harvey Wu
Warren Mo
I. Chattopadhyay
Hod Lipson
BDL
8
84
0
02 Mar 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth
  Concurrency Analysis
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Tal Ben-Nun
Torsten Hoefler
GNN
33
703
0
26 Feb 2018
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing
  Atari
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
P. Chrabaszcz
I. Loshchilov
Frank Hutter
32
99
0
24 Feb 2018
Structured Control Nets for Deep Reinforcement Learning
Structured Control Nets for Deep Reinforcement Learning
Mario Srouji
Jian Zhang
Ruslan Salakhutdinov
30
43
0
22 Feb 2018
Diversity is All You Need: Learning Skills without a Reward Function
Diversity is All You Need: Learning Skills without a Reward Function
Benjamin Eysenbach
Abhishek Gupta
Julian Ibarz
Sergey Levine
22
1,061
0
16 Feb 2018
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement
  Learning Algorithms
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Cédric Colas
Olivier Sigaud
Pierre-Yves Oudeyer
26
157
0
14 Feb 2018
Regularized Evolution for Image Classifier Architecture Search
Regularized Evolution for Image Classifier Architecture Search
Esteban Real
A. Aggarwal
Yanping Huang
Quoc V. Le
13
3,000
0
05 Feb 2018
GitGraph - Architecture Search Space Creation through Frequent
  Computational Subgraph Mining
GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining
Kamil Bennani-Smires
C. Musat
Andreea Hossmann
Michael Baeriswyl
GNN
18
0
0
16 Jan 2018
ES Is More Than Just a Traditional Finite-Difference Approximator
ES Is More Than Just a Traditional Finite-Difference Approximator
Joel Lehman
Jay Chen
Jeff Clune
Kenneth O. Stanley
17
89
0
18 Dec 2017
On the Relationship Between the OpenAI Evolution Strategy and Stochastic
  Gradient Descent
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent
Xingwen Zhang
Jeff Clune
Kenneth O. Stanley
20
57
0
18 Dec 2017
Safe Mutations for Deep and Recurrent Neural Networks through Output
  Gradients
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
Joel Lehman
Jay Chen
Jeff Clune
Kenneth O. Stanley
25
93
0
18 Dec 2017
Improving Exploration in Evolution Strategies for Deep Reinforcement
  Learning via a Population of Novelty-Seeking Agents
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Edoardo Conti
Vashisht Madhavan
F. Such
Joel Lehman
Kenneth O. Stanley
Jeff Clune
11
342
0
18 Dec 2017
Deep Learning for Video Game Playing
Deep Learning for Video Game Playing
Niels Justesen
Philip Bontrager
Julian Togelius
S. Risi
VLM
24
206
0
25 Aug 2017
Scalable Training of Artificial Neural Networks with Adaptive Sparse
  Connectivity inspired by Network Science
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
Decebal Constantin Mocanu
Elena Mocanu
Peter Stone
Phuong H. Nguyen
M. Gibescu
A. Liotta
19
609
0
15 Jul 2017
Evolving Deep Neural Networks
Evolving Deep Neural Networks
Risto Miikkulainen
J. Liang
Elliot Meyerson
Aditya Rawal
Daniel Fink
...
B. Raju
H. Shahrzad
Arshak Navruzyan
Nigel P. Duffy
B. Hodjat
18
884
0
01 Mar 2017
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINN
AI4CE
ODL
75
2,750
0
20 Feb 2015
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