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Retrospective Analysis of the 2019 MineRL Competition on Sample
  Efficient Reinforcement Learning

Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning

10 March 2020
Stephanie Milani
Nicholay Topin
Brandon Houghton
William H. Guss
Sharada Mohanty
Keisuke Nakata
Oriol Vinyals
N. Kuno
    OffRL
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Papers citing "Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning"

8 / 8 papers shown
Title
Academic competitions
Academic competitions
H. J. Escalante
Aleksandra Kruchinina
AI4CE
30
0
0
01 Dec 2023
Benchmarking Robustness and Generalization in Multi-Agent Systems: A
  Case Study on Neural MMO
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
Yangkun Chen
Joseph Suárez
Junjie Zhang
Chenghui Yu
Bo Wu
...
Sharada Mohanty
Jiaxin Chen
Xiu Li
Xiaolong Zhu
Phillip Isola
29
0
0
30 Aug 2023
Learning to Generalize with Object-centric Agents in the Open World
  Survival Game Crafter
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter
Aleksandar Stanić
Yujin Tang
David R Ha
Jürgen Schmidhuber
ELM
29
13
0
05 Aug 2022
Benchmarking the Spectrum of Agent Capabilities
Benchmarking the Spectrum of Agent Capabilities
Danijar Hafner
ELM
33
127
0
14 Sep 2021
Accelerating the Learning of TAMER with Counterfactual Explanations
Accelerating the Learning of TAMER with Counterfactual Explanations
Jakob Karalus
F. Lindner
OffRL
29
4
0
03 Aug 2021
Iterative Bounding MDPs: Learning Interpretable Policies via
  Non-Interpretable Methods
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Nicholay Topin
Stephanie Milani
Fei Fang
Manuela Veloso
OffRL
16
32
0
25 Feb 2021
String Diagrams for Assembly Planning
String Diagrams for Assembly Planning
J. Master
Evan Patterson
Shahin Yousfi
A. Canedo
11
6
0
23 Sep 2019
Learning to Run challenge: Synthesizing physiologically accurate motion
  using deep reinforcement learning
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
L. Kidzinski
Sharada Mohanty
Carmichael F. Ong
Jennifer Hicks
Sean F. Carroll
Sergey Levine
M. Salathé
Scott L. Delp
19
60
0
31 Mar 2018
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