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When to use parametric models in reinforcement learning?

When to use parametric models in reinforcement learning?

12 June 2019
H. V. Hasselt
Matteo Hessel
John Aslanides
ArXivPDFHTML

Papers citing "When to use parametric models in reinforcement learning?"

24 / 124 papers shown
Title
Forethought and Hindsight in Credit Assignment
Forethought and Hindsight in Credit Assignment
Veronica Chelu
Doina Precup
H. V. Hasselt
22
25
0
26 Oct 2020
Masked Contrastive Representation Learning for Reinforcement Learning
Masked Contrastive Representation Learning for Reinforcement Learning
Jinhua Zhu
Yingce Xia
Lijun Wu
Jiajun Deng
Wen-gang Zhou
Tao Qin
Houqiang Li
SSL
OffRL
34
55
0
15 Oct 2020
Local Search for Policy Iteration in Continuous Control
Local Search for Policy Iteration in Continuous Control
Jost Tobias Springenberg
N. Heess
D. Mankowitz
J. Merel
Arunkumar Byravan
...
Julian Schrittwieser
Yuval Tassa
J. Buchli
Dan Belov
Martin Riedmiller
OffRL
22
15
0
12 Oct 2020
Offline Learning for Planning: A Summary
Offline Learning for Planning: A Summary
Giorgio Angelotti
Nicolas Drougard
Caroline Ponzoni Carvalho Chanel
OffRL
22
4
0
05 Oct 2020
On the model-based stochastic value gradient for continuous
  reinforcement learning
On the model-based stochastic value gradient for continuous reinforcement learning
Brandon Amos
Samuel Stanton
Denis Yarats
A. Wilson
24
71
0
28 Aug 2020
Improved Memories Learning
Improved Memories Learning
Francesco Varoli
G. Novati
Pantelis R. Vlachas
Petros Koumoutsakos
OffRL
AI4TS
9
0
0
24 Aug 2020
Understanding and Mitigating the Limitations of Prioritized Experience
  Replay
Understanding and Mitigating the Limitations of Prioritized Experience Replay
Yangchen Pan
Jincheng Mei
Amir-massoud Farahmand
Martha White
Hengshuai Yao
Mohsen Rohani
Jun Luo
24
11
0
19 Jul 2020
Learning to Sample with Local and Global Contexts in Experience Replay
  Buffer
Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Youngmin Oh
Kimin Lee
Jinwoo Shin
Eunho Yang
Sung Ju Hwang
OffRL
9
16
0
14 Jul 2020
Revisiting Fundamentals of Experience Replay
Revisiting Fundamentals of Experience Replay
W. Fedus
Prajit Ramachandran
Rishabh Agarwal
Yoshua Bengio
Hugo Larochelle
Mark Rowland
Will Dabney
KELM
OffRL
30
235
0
13 Jul 2020
Data-Efficient Reinforcement Learning with Self-Predictive
  Representations
Data-Efficient Reinforcement Learning with Self-Predictive Representations
Max Schwarzer
Ankesh Anand
Rishab Goel
R. Devon Hjelm
Aaron Courville
Philip Bachman
41
312
0
12 Jul 2020
Learning Retrospective Knowledge with Reverse Reinforcement Learning
Learning Retrospective Knowledge with Reverse Reinforcement Learning
Shangtong Zhang
Vivek Veeriah
Shimon Whiteson
OffRL
AI4TS
16
13
0
09 Jul 2020
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep
  Reinforcement Learning
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Kimin Lee
Michael Laskin
A. Srinivas
Pieter Abbeel
OffRL
25
199
0
09 Jul 2020
Selective Dyna-style Planning Under Limited Model Capacity
Selective Dyna-style Planning Under Limited Model Capacity
Zaheer Abbas
Samuel Sokota
Erin J. Talvitie
Martha White
37
32
0
05 Jul 2020
Expected Eligibility Traces
Expected Eligibility Traces
H. V. Hasselt
Sephora Madjiheurem
Matteo Hessel
David Silver
André Barreto
Diana Borsa
15
37
0
03 Jul 2020
Model-based Reinforcement Learning: A Survey
Model-based Reinforcement Learning: A Survey
Thomas M. Moerland
Joost Broekens
Aske Plaat
Catholijn M. Jonker
OffRL
36
47
0
30 Jun 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy
  Search and Planning
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Sebastian Curi
Felix Berkenkamp
Andreas Krause
33
82
0
15 Jun 2020
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect
  Environment Models
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models
Taher Jafferjee
Ehsan Imani
Erin J. Talvitie
Martha White
Micheal Bowling
20
32
0
08 Jun 2020
Acme: A Research Framework for Distributed Reinforcement Learning
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman
Bobak Shahriari
John Aslanides
Gabriel Barth-Maron
Nikola Momchev
...
Srivatsan Srinivasan
A. Cowie
Ziyun Wang
Bilal Piot
Nando de Freitas
65
225
0
01 Jun 2020
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement
  Learning
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
Simone Totaro
Ioannis Boukas
Anders Jonsson
Bertrand Cornélusse
6
30
0
16 May 2020
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator
  Policy Optimization
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
P. DÓro
Wojciech Ja'skowski
OffRL
30
27
0
29 Apr 2020
Image Augmentation Is All You Need: Regularizing Deep Reinforcement
  Learning from Pixels
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
Ilya Kostrikov
Denis Yarats
Rob Fergus
OffRL
45
774
0
28 Apr 2020
CURL: Contrastive Unsupervised Representations for Reinforcement
  Learning
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
A. Srinivas
Michael Laskin
Pieter Abbeel
SSL
DRL
OffRL
49
1,063
0
08 Apr 2020
Biologically inspired architectures for sample-efficient deep
  reinforcement learning
Biologically inspired architectures for sample-efficient deep reinforcement learning
Pierre Harvey Richemond
Arinbjorn Kolbeinsson
Yike Guo
27
2
0
25 Nov 2019
Model-Based Reinforcement Learning for Atari
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser
Mohammad Babaeizadeh
Piotr Milos
B. Osinski
R. Campbell
...
Sergey Levine
Afroz Mohiuddin
Ryan Sepassi
George Tucker
Henryk Michalewski
OffRL
29
843
0
01 Mar 2019
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