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Can Learned Optimization Make Reinforcement Learning Less Difficult?
9 July 2024
Alexander David Goldie
Chris Xiaoxuan Lu
Matthew Jackson
Shimon Whiteson
Jakob N. Foerster
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Papers citing
"Can Learned Optimization Make Reinforcement Learning Less Difficult?"
6 / 6 papers shown
Title
Learning Versatile Optimizers on a Compute Diet
A. Moudgil
Boris Knyazev
Guillaume Lajoie
Eugene Belilovsky
56
0
0
22 Jan 2025
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks
Michael T. Matthews
Michael Beukman
Chris Xiaoxuan Lu
Jakob Foerster
OffRL
AI4CE
36
2
0
30 Oct 2024
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
H. V. Hasselt
Razvan Pascanu
James Martens
Will Dabney
AI4CE
50
30
0
29 Feb 2024
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
Hojoon Lee
Hanseul Cho
Hyunseung Kim
Daehoon Gwak
Joonkee Kim
Jaegul Choo
Se-Young Yun
Chulhee Yun
OffRL
69
25
0
19 Jun 2023
The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin
Max Schwarzer
P. DÓro
Pierre-Luc Bacon
Aaron C. Courville
OnRL
85
178
0
16 May 2022
Training language models to follow instructions with human feedback
Long Ouyang
Jeff Wu
Xu Jiang
Diogo Almeida
Carroll L. Wainwright
...
Amanda Askell
Peter Welinder
Paul Christiano
Jan Leike
Ryan J. Lowe
OSLM
ALM
301
11,730
0
04 Mar 2022
1