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Learning to Run challenge: Synthesizing physiologically accurate motion
  using deep reinforcement learning

Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning

31 March 2018
L. Kidzinski
Sharada Mohanty
Carmichael F. Ong
Jennifer Hicks
Sean F. Carroll
Sergey Levine
M. Salathé
Scott L. Delp
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Papers citing "Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning"

20 / 20 papers shown
Title
Motion Control of High-Dimensional Musculoskeletal Systems with Hierarchical Model-Based Planning
Motion Control of High-Dimensional Musculoskeletal Systems with Hierarchical Model-Based Planning
Yunyue Wei
Shanning Zhuang
Vincent Zhuang
Yanan Sui
29
0
0
13 May 2025
A Survey on Physics Informed Reinforcement Learning: Review and Open
  Problems
A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
C. Banerjee
Kien Nguyen
Clinton Fookes
M. Raissi
PINN
AI4CE
28
9
0
05 Sep 2023
Ten Steps to Becoming a Musculoskeletal Simulation Expert: A
  Half-Century of Progress and Outlook for the Future
Ten Steps to Becoming a Musculoskeletal Simulation Expert: A Half-Century of Progress and Outlook for the Future
Scott Uhlrich
Thomas K. Uchida
Marissa R. Lee
Scott L. Delp
MedIm
AI4CE
34
20
0
01 Jun 2023
A Survey on Reinforcement Learning Methods in Character Animation
A Survey on Reinforcement Learning Methods in Character Animation
Ariel Kwiatkowski
Eduardo Alvarado
Vicky Kalogeiton
Chenxi Liu
Julien Pettré
M. van de Panne
Marie-Paule Cani
AI4CE
24
44
0
07 Mar 2022
Measuring Sample Efficiency and Generalization in Reinforcement Learning
  Benchmarks: NeurIPS 2020 Procgen Benchmark
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty
Jyotish Poonganam
Adrien Gaidon
Andrey Kolobov
Blake Wulfe
...
Jacob Hilton
William H. Guss
Sahika Genc
John Schulman
K. Cobbe
28
22
0
29 Mar 2021
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning
  using Human Priors
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss
Cayden R. Codel
Katja Hofmann
Brandon Houghton
Noburu Kuno
...
John Schulman
Manuela Veloso
Nicholay Topin
Avinash Ummadisingu
Phillip Wang
OffRL
17
63
0
26 Jan 2021
Learning to Run with Potential-Based Reward Shaping and Demonstrations
  from Video Data
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
Aleksandra Malysheva
D. Kudenko
A. Shpilman
29
5
0
16 Dec 2020
Biomechanic Posture Stabilisation via Iterative Training of Multi-policy
  Deep Reinforcement Learning Agents
Biomechanic Posture Stabilisation via Iterative Training of Multi-policy Deep Reinforcement Learning Agents
M. Hossny
Julie Iskander
29
0
0
21 Aug 2020
An ocular biomechanics environment for reinforcement learning
An ocular biomechanics environment for reinforcement learning
Julie Iskander
M. Hossny
16
3
0
12 Aug 2020
Reinforcement Learning of Musculoskeletal Control from Functional
  Simulations
Reinforcement Learning of Musculoskeletal Control from Functional Simulations
Emanuel Joos
Fabien Péan
Orçun Göksel
AI4CE
22
12
0
13 Jul 2020
Refined Continuous Control of DDPG Actors via Parametrised Activation
Refined Continuous Control of DDPG Actors via Parametrised Activation
M. Hossny
Julie Iskander
Mohammed Attia
Khaled Saleh
17
7
0
04 Jun 2020
Retrospective Analysis of the 2019 MineRL Competition on Sample
  Efficient Reinforcement Learning
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning
Stephanie Milani
Nicholay Topin
Brandon Houghton
William H. Guss
Sharada Mohanty
Keisuke Nakata
Oriol Vinyals
N. Kuno
OffRL
41
27
0
10 Mar 2020
Learning to run a power network challenge for training topology
  controllers
Learning to run a power network challenge for training topology controllers
Antoine Marot
Benjamin Donnot
Camilo Romero
Luca Veyrin-Forrer
Marvin Lerousseau
Balthazar Donon
Isabelle M Guyon
16
75
0
05 Dec 2019
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire
  Evacuation Environment
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
Jivitesh Sharma
Per-Arne Andersen
Ole-Christoffer Granmo
M. G. Olsen
AI4CE
24
68
0
23 May 2019
Synthesis of Biologically Realistic Human Motion Using Joint Torque
  Actuation
Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation
Yifeng Jiang
Tom Van Wouwe
F. D. Groote
Chenxi Liu
30
75
0
30 Apr 2019
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning
  using Human Priors
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss
Cayden R. Codel
Katja Hofmann
Brandon Houghton
Noburu Kuno
...
Diego Perez Liebana
Ruslan Salakhutdinov
Nicholay Topin
Manuela Veloso
Phillip Wang
OffRL
36
65
0
22 Apr 2019
The AI Driving Olympics at NeurIPS 2018
The AI Driving Olympics at NeurIPS 2018
J. Zilly
J. Tani
Breandan Considine
Bhairav Mehta
Andrea F. Daniele
...
R. Hristov
S. Mallya
Emilio Frazzoli
A. Censi
Liam Paull
18
14
0
06 Mar 2019
Artificial Intelligence for Prosthetics - challenge solutions
Artificial Intelligence for Prosthetics - challenge solutions
L. Kidzinski
Carmichael F. Ong
Sharada Mohanty
Jennifer Hicks
Sean F. Carroll
...
E. Tumer
J. Watson
M. Salathé
Sergey Levine
Scott L. Delp
15
40
0
07 Feb 2019
Variational Inference for Data-Efficient Model Learning in POMDPs
Variational Inference for Data-Efficient Model Learning in POMDPs
Sebastian Tschiatschek
Kai Arulkumaran
Jan Stühmer
Katja Hofmann
24
15
0
23 May 2018
Learning to Run challenge solutions: Adapting reinforcement learning
  methods for neuromusculoskeletal environments
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
L. Kidzinski
Sharada Mohanty
Carmichael F. Ong
Zhewei Huang
Shuchang Zhou
...
Sean F. Carroll
Jennifer Hicks
Sergey Levine
M. Salathé
Scott L. Delp
34
88
0
02 Apr 2018
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