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Deterministic Implementations for Reproducibility in Deep Reinforcement
  Learning

Deterministic Implementations for Reproducibility in Deep Reinforcement Learning

15 September 2018
P. Nagarajan
Garrett A. Warnell
Peter Stone
ArXivPDFHTML

Papers citing "Deterministic Implementations for Reproducibility in Deep Reinforcement Learning"

13 / 13 papers shown
Title
Studying the Impact of TensorFlow and PyTorch Bindings on Machine
  Learning Software Quality
Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality
Hao Li
Gopi Krishnan Rajbahadur
C. Bezemer
39
5
0
07 Jul 2024
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Harald Semmelrock
Tony Ross-Hellauer
Simone Kopeinik
Dieter Theiler
Armin Haberl
Stefan Thalmann
Dominik Kowald
65
6
0
20 Jun 2024
Reproducibility in Machine Learning-Driven Research
Reproducibility in Machine Learning-Driven Research
Harald Semmelrock
Simone Kopeinik
Dieter Theiler
Tony Ross-Hellauer
Dominik Kowald
AI4CE
14
15
0
19 Jul 2023
EasyScale: Accuracy-consistent Elastic Training for Deep Learning
EasyScale: Accuracy-consistent Elastic Training for Deep Learning
Mingzhen Li
Wencong Xiao
Biao Sun
Hanyu Zhao
Hailong Yang
...
Xianyan Jia
Yi Liu
Yong Li
Wei Lin
D. Qian
22
7
0
30 Aug 2022
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal
Max Schwarzer
Pablo Samuel Castro
Aaron Courville
Marc G. Bellemare
OffRL
59
637
0
30 Aug 2021
Generalization Guarantees for Neural Architecture Search with
  Train-Validation Split
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Samet Oymak
Mingchen Li
Mahdi Soltanolkotabi
AI4CE
OOD
36
13
0
29 Apr 2021
mlf-core: a framework for deterministic machine learning
mlf-core: a framework for deterministic machine learning
Lukas Heumos
Philipp Ehmele
Luis Kuhn Cuellar
Kevin Menden
Edmund Miller
Steffen Lemke
G. Gabernet
S. Nahnsen
AI4CE
18
3
0
15 Apr 2021
A Study of Checkpointing in Large Scale Training of Deep Neural Networks
A Study of Checkpointing in Large Scale Training of Deep Neural Networks
Elvis Rojas
A. Kahira
Esteban Meneses
L. Bautista-Gomez
Rosa M. Badia
29
22
0
01 Dec 2020
Anti-Distillation: Improving reproducibility of deep networks
Anti-Distillation: Improving reproducibility of deep networks
G. Shamir
Lorenzo Coviello
42
20
0
19 Oct 2020
The Scientific Method in the Science of Machine Learning
The Scientific Method in the Science of Machine Learning
Jessica Zosa Forde
Michela Paganini
24
35
0
24 Apr 2019
Machine Learning for Combinatorial Optimization: a Methodological Tour
  d'Horizon
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
Yoshua Bengio
Andrea Lodi
Antoine Prouvost
86
1,353
0
15 Nov 2018
A Survey and Critique of Multiagent Deep Reinforcement Learning
A Survey and Critique of Multiagent Deep Reinforcement Learning
Pablo Hernandez-Leal
Bilal Kartal
Matthew E. Taylor
OffRL
32
550
0
12 Oct 2018
Emergence of Locomotion Behaviours in Rich Environments
Emergence of Locomotion Behaviours in Rich Environments
N. Heess
TB Dhruva
S. Sriram
Jay Lemmon
J. Merel
...
Tom Erez
Ziyun Wang
S. M. Ali Eslami
Martin Riedmiller
David Silver
143
928
0
07 Jul 2017
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