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Physically Embedded Planning Problems: New Challenges for Reinforcement
  Learning

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning

11 September 2020
M. Berk Mirza
Andrew Jaegle
Jonathan J. Hunt
A. Guez
S. Tunyasuvunakool
Alistair Muldal
T. Weber
Peter Karkus
S. Racanière
Lars Buesing
Timothy Lillicrap
N. Heess
    AI4CE
ArXivPDFHTML

Papers citing "Physically Embedded Planning Problems: New Challenges for Reinforcement Learning"

5 / 5 papers shown
Title
Physical Derivatives: Computing policy gradients by physical
  forward-propagation
Physical Derivatives: Computing policy gradients by physical forward-propagation
Arash Mehrjou
Ashkan Soleymani
Stefan Bauer
Bernhard Schölkopf
38
0
0
15 Jan 2022
Pushing it out of the Way: Interactive Visual Navigation
Pushing it out of the Way: Interactive Visual Navigation
Kuo-Hao Zeng
Luca Weihs
Ali Farhadi
Roozbeh Mottaghi
26
30
0
28 Apr 2021
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and
  Transfer Learning
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Ossama Ahmed
Frederik Trauble
Anirudh Goyal
Alexander Neitz
Yoshua Bengio
Bernhard Schölkopf
M. Wuthrich
Stefan Bauer
CML
37
120
0
08 Oct 2020
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based
  Character Skills
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
Xue Bin Peng
Pieter Abbeel
Sergey Levine
M. van de Panne
AI4CE
175
494
0
08 Apr 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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