Activity
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Challenges Entered
Machine Learning for detection of early onset of Alzheimers
Latest submissions
Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments
Latest submissions
See Allfailed | 81668 | ||
failed | 81667 | ||
failed | 81666 |
Play in a realistic insurance market, compete for profit!
Latest submissions
A dataset and open-ended challenge for music recommendation research
Latest submissions
A benchmark for image-based food recognition
Latest submissions
Sample-efficient reinforcement learning in Minecraft
Latest submissions
Reinforcement Learning on Musculoskeletal Models
Latest submissions
See Allgraded | 23318 | ||
graded | 23317 | ||
graded | 23314 |
Sample-efficient reinforcement learning in Minecraft
Latest submissions
Robots that learn to interact with the environment autonomously
Latest submissions
See Allgraded | 21845 |
Participant | Rating |
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Participant | Rating |
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NeurIPS 2020: Procgen Competition
NeurIPS 2019: Learn to Move - Walk Around
Raw observation usage β Osim
Over 5 years agoHi,
Is it possible to use osim dict observation, rather than proposed projection?
Thanks!
Catalyst starter kit
Over 5 years agoHi,
What?
I just want to share with you my starter kit for this year competition - distributed DDPG agent based on Catalyst.RL framework.
Why I am doing this?
- I am participating in this competition for last 2 year. And I love it, I think, that the competition mission is really important and valuable.
- I already have 2 publicly available solutions (both are 3rd place winners):
2017 - https://github.com/Scitator/Run-Skeleton-Run
2018 - https://github.com/Scitator/neurips-18-prosthetics-challenge - last year I also open-sourced Catalyst - DL&RL framework for fast and reproducible experiments. The RL part was developed during 2018 year competition.
Based on all this I want to boost community activity in this competition and RL overall.
For more RL news and this competition insights - you can find me by twitter.
Thanks!
FAQ: Round 1 evaluations configuration
Over 4 years agoDear organisers,
Is it possible to remove ray dependency from second round?
From my perspective Ray looks like a too heavy solution for this task.
Do you have any pure TF/Pytorch baselines to start from?