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Unifying Count-Based Exploration and Intrinsic Motivation

Unifying Count-Based Exploration and Intrinsic Motivation

6 June 2016
Marc G. Bellemare
S. Srinivasan
Georg Ostrovski
Tom Schaul
D. Saxton
Rémi Munos
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Papers citing "Unifying Count-Based Exploration and Intrinsic Motivation"

34 / 334 papers shown
Title
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Leshem Choshen
Lior Fox
Y. Loewenstein
OffRL
13
62
0
11 Apr 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
Some Considerations on Learning to Explore via Meta-Reinforcement
  Learning
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Bradly C. Stadie
Ge Yang
Rein Houthooft
Xi Chen
Yan Duan
Yuhuai Wu
Pieter Abbeel
Ilya Sutskever
LRM
29
116
0
03 Mar 2018
Computational Theories of Curiosity-Driven Learning
Computational Theories of Curiosity-Driven Learning
Pierre-Yves Oudeyer
29
64
0
28 Feb 2018
Investigating Human Priors for Playing Video Games
Investigating Human Priors for Playing Video Games
Rachit Dubey
Pulkit Agrawal
Deepak Pathak
Thomas Griffiths
Alexei A. Efros
OffRL
33
144
0
28 Feb 2018
The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation
The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation
Simon Lucas
Jialin Liu
Diego Perez-Liebana
18
47
0
16 Feb 2018
Building machines that adapt and compute like brains
Building machines that adapt and compute like brains
Brenden M. Lake
J. Tenenbaum
AI4CE
FedML
NAI
AILaw
254
890
0
11 Nov 2017
Exploration in Feature Space for Reinforcement Learning
Exploration in Feature Space for Reinforcement Learning
S. N. Sasikumar
52
4
0
05 Oct 2017
Deep Abstract Q-Networks
Deep Abstract Q-Networks
Melrose Roderick
Christopher Grimm
Stefanie Tellex
35
33
0
02 Oct 2017
Revisiting the Arcade Learning Environment: Evaluation Protocols and
  Open Problems for General Agents
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
Marlos C. Machado
Marc G. Bellemare
Erik Talvitie
J. Veness
Matthew J. Hausknecht
Michael Bowling
21
544
0
18 Sep 2017
The Uncertainty Bellman Equation and Exploration
The Uncertainty Bellman Equation and Exploration
Brendan O'Donoghue
Ian Osband
Rémi Munos
Volodymyr Mnih
19
183
0
15 Sep 2017
A Brief Survey of Deep Reinforcement Learning
A Brief Survey of Deep Reinforcement Learning
Kai Arulkumaran
M. Deisenroth
Miles Brundage
Anil Anthony Bharath
OffRL
59
2,776
0
19 Aug 2017
Trial without Error: Towards Safe Reinforcement Learning via Human
  Intervention
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
William Saunders
Girish Sastry
Andreas Stuhlmuller
Owain Evans
OffRL
24
229
0
17 Jul 2017
Hindsight Experience Replay
Hindsight Experience Replay
Marcin Andrychowicz
Dwight Crow
Alex Ray
Jonas Schneider
Rachel Fong
Peter Welinder
Bob McGrew
Joshua Tobin
Pieter Abbeel
Wojciech Zaremba
OffRL
87
2,296
0
05 Jul 2017
Count-Based Exploration in Feature Space for Reinforcement Learning
Count-Based Exploration in Feature Space for Reinforcement Learning
Jarryd Martin
S. N. Sasikumar
Tom Everitt
Marcus Hutter
24
122
0
25 Jun 2017
Dex: Incremental Learning for Complex Environments in Deep Reinforcement
  Learning
Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning
Nick Erickson
Qi Zhao
CLL
OffRL
181
2
0
19 Jun 2017
Universal Reinforcement Learning Algorithms: Survey and Experiments
Universal Reinforcement Learning Algorithms: Survey and Experiments
John Aslanides
Jan Leike
Marcus Hutter
OffRL
26
19
0
30 May 2017
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement
  Learning
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
Nat Dilokthanakul
Christos Kaplanis
Nick Pawlowski
Murray Shanahan
24
91
0
18 May 2017
Automatic Goal Generation for Reinforcement Learning Agents
Automatic Goal Generation for Reinforcement Learning Agents
Carlos Florensa
David Held
Xinyang Geng
Pieter Abbeel
78
499
0
17 May 2017
Curiosity-driven Exploration by Self-supervised Prediction
Curiosity-driven Exploration by Self-supervised Prediction
Deepak Pathak
Pulkit Agrawal
Alexei A. Efros
Trevor Darrell
LRM
SSL
46
2,394
0
15 May 2017
From Language to Programs: Bridging Reinforcement Learning and Maximum
  Marginal Likelihood
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
Kelvin Guu
Panupong Pasupat
E. Liu
Percy Liang
34
190
0
25 Apr 2017
Beating Atari with Natural Language Guided Reinforcement Learning
Beating Atari with Natural Language Guided Reinforcement Learning
Russell Kaplan
Chris Sauer
A. Sosa
LM&Ro
19
69
0
18 Apr 2017
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Carlos Florensa
Yan Duan
Pieter Abbeel
BDL
47
360
0
10 Apr 2017
Deep Exploration via Randomized Value Functions
Deep Exploration via Randomized Value Functions
Ian Osband
Benjamin Van Roy
Daniel Russo
Zheng Wen
41
300
0
22 Mar 2017
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Joshua Achiam
S. Shankar Sastry
29
235
0
06 Mar 2017
Count-Based Exploration with Neural Density Models
Count-Based Exploration with Neural Density Models
Georg Ostrovski
Marc G. Bellemare
Aaron van den Oord
Rémi Munos
50
614
0
03 Mar 2017
Deep Reinforcement Learning: An Overview
Deep Reinforcement Learning: An Overview
Yuxi Li
OffRL
VLM
104
1,503
0
25 Jan 2017
Variational Intrinsic Control
Variational Intrinsic Control
Karol Gregor
Danilo Jimenez Rezende
Daan Wierstra
DRL
OffRL
19
425
0
22 Nov 2016
A Deep Learning Approach for Joint Video Frame and Reward Prediction in
  Atari Games
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games
Felix Leibfried
Nate Kushman
Katja Hofmann
46
43
0
21 Nov 2016
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement
  Learning
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang
Rein Houthooft
Davis Foote
Adam Stooke
Xi Chen
Yan Duan
John Schulman
F. Turck
Pieter Abbeel
OffRL
60
760
0
15 Nov 2016
Playing SNES in the Retro Learning Environment
Playing SNES in the Retro Learning Environment
Nadav Bhonker
Shai Rozenberg
Itay Hubara
18
19
0
07 Nov 2016
Supervision via Competition: Robot Adversaries for Learning Tasks
Supervision via Competition: Robot Adversaries for Learning Tasks
Lerrel Pinto
James Davidson
Abhinav Gupta
SSL
24
82
0
05 Oct 2016
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for
  Task-Oriented Dialogue Systems
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Zachary Chase Lipton
Xiujun Li
Jianfeng Gao
Lihong Li
Faisal Ahmed
Li Deng
24
6
0
17 Aug 2016
Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord
Nal Kalchbrenner
Koray Kavukcuoglu
SSeg
GAN
272
2,552
0
25 Jan 2016
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