ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1706.10295
  4. Cited By
Noisy Networks for Exploration

Noisy Networks for Exploration

30 June 2017
Meire Fortunato
M. G. Azar
Bilal Piot
Jacob Menick
Ian Osband
Alex Graves
Vlad Mnih
Rémi Munos
Demis Hassabis
Olivier Pietquin
Charles Blundell
Shane Legg
ArXivPDFHTML

Papers citing "Noisy Networks for Exploration"

15 / 165 papers shown
Title
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep
  Learning
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
W. Wen
Yandan Wang
Feng Yan
Cong Xu
Chunpeng Wu
Yiran Chen
H. Li
24
50
0
21 May 2018
Exploration by Distributional Reinforcement Learning
Exploration by Distributional Reinforcement Learning
Yunhao Tang
Shipra Agrawal
OOD
41
30
0
04 May 2018
Imitation Learning with Concurrent Actions in 3D Games
Imitation Learning with Concurrent Actions in 3D Games
Jack Harmer
Linus Gisslén
Jorge del Val
Henrik Holst
Joakim Bergdahl
Tom Olsson
K. Sjöö
Magnus Nordin
21
45
0
14 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
Flipout: Efficient Pseudo-Independent Weight Perturbations on
  Mini-Batches
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen
Paul Vicol
Jimmy Ba
Dustin Tran
Roger C. Grosse
BDL
22
307
0
12 Mar 2018
Variance Networks: When Expectation Does Not Meet Your Expectations
Variance Networks: When Expectation Does Not Meet Your Expectations
Kirill Neklyudov
Dmitry Molchanov
Arsenii Ashukha
Dmitry Vetrov
UQCV
20
23
0
10 Mar 2018
Accelerated Methods for Deep Reinforcement Learning
Accelerated Methods for Deep Reinforcement Learning
Adam Stooke
Pieter Abbeel
OffRL
OnRL
25
133
0
07 Mar 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep
  Networks for Thompson Sampling
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
C. Riquelme
George Tucker
Jasper Snoek
BDL
41
365
0
26 Feb 2018
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
F. Such
Vashisht Madhavan
Edoardo Conti
Joel Lehman
Kenneth O. Stanley
Jeff Clune
47
686
0
18 Dec 2017
Variational Deep Q Network
Variational Deep Q Network
Yunhao Tang
A. Kucukelbir
BDL
38
10
0
30 Nov 2017
AI Safety Gridworlds
AI Safety Gridworlds
Jan Leike
Miljan Martic
Victoria Krakovna
Pedro A. Ortega
Tom Everitt
Andrew Lefrancq
Laurent Orseau
Shane Legg
44
250
0
27 Nov 2017
Super-Convergence: Very Fast Training of Neural Networks Using Large
  Learning Rates
Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
L. Smith
Nicholay Topin
AI4CE
48
519
0
23 Aug 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
Deep Reinforcement Learning: An Overview
Deep Reinforcement Learning: An Overview
Yuxi Li
OffRL
VLM
104
1,505
0
25 Jan 2017
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
287
9,167
0
06 Jun 2015
Previous
1234