Activity
Ratings Progression
Challenge Categories
Challenges Entered
Revolutionise E-Commerce with LLM!
Latest submissions
Revolutionising Interior Design with AI
Latest submissions
A benchmark for image-based food recognition
Latest submissions
Latest submissions
Training sample-efficient agents in Minecraft
Latest submissions
Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments
Latest submissions
See Allgraded | 67944 | ||
graded | 67942 |
Multi-Agent Reinforcement Learning on Trains
Latest submissions
See Allfailed | 99379 | ||
graded | 99368 | ||
graded | 99359 |
A benchmark for image-based food recognition
Latest submissions
Multi-agent RL in game environment. Train your Derklings, creatures with a neural network brain, to fight for you!
Latest submissions
Classify images of snake species from around the world
Latest submissions
Reinforcement Learning on Musculoskeletal Models
Latest submissions
Sample-efficient reinforcement learning in Minecraft
Latest submissions
See Allgraded | 25410 | ||
graded | 25409 | ||
failed | 25399 |
Multi Agent Reinforcement Learning on Trains.
Latest submissions
See Allfailed | 67801 | ||
failed | 67786 | ||
failed | 67761 |
Latest submissions
5 Problems 15 Days. Can you solve it all?
Latest submissions
Project 2: Road extraction from satellite images
Latest submissions
Project 2: build our own text classifier system, and test its performance.
Latest submissions
A new benchmark for Artificial Intelligence (AI) research in Reinforcement Learning
Latest submissions
Predict if users will skip or listen to the music they're streamed
Latest submissions
Participant | Rating |
---|---|
shivam | 136 |
anssi | 221 |
Participant | Rating |
---|---|
junjie_li | 308 |
anssi | 221 |
manueth | 0 |
thomas_simonini | 0 |
π Flatland Workshop
Flatland Workshop-b33f21
About the Flatland Workshop-b33f21 category
Over 3 years ago(Replace this first paragraph with a brief description of your new category. This guidance will appear in the category selection area, so try to keep it below 200 characters.)
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Flatland Workshop
About the Flatland Workshop category
Over 3 years ago(Replace this first paragraph with a brief description of your new category. This guidance will appear in the category selection area, so try to keep it below 200 characters.)
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Flatland
Editor.py missing package jpy_canvas
Almost 4 years agoMetadata.csv is missing from the latest testset
Almost 4 years agoHello Sahil, which archive are you referring to? I see the metadata.csv
in both test-neurips2020-round2-v0.tar.gz
and test-neurips2020-round1-v1.tar.gz
from the Resources page.
edit: This was an error in the doc that weβve now corrected, thanks for the report!
π The Flatland paper is out!
Almost 4 years agoA first version of the Flatland competition paper is out on arXiv!
Flatland-RL : Multi-Agent Reinforcement Learning on Trains
@misc{mohanty2020flatlandrl,
title={Flatland-RL : Multi-Agent Reinforcement Learning on Trains},
author={Sharada Mohanty and Erik Nygren and Florian Laurent and Manuel Schneider and Christian Scheller and Nilabha Bhattacharya and Jeremy Watson and Adrian Egli and Christian Eichenberger and Christian Baumberger and Gereon Vienken and Irene Sturm and Guillaume Sartoretti and Giacomo Spigler},
year={2020},
eprint={2012.05893},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
A separate paper will cover competition results, more information about it coming soon.
πΉ Flatland NeurIPS Townhall
Almost 4 years agoHey @jakob_stigenberg, yes the event was recorded, we will release the video publicly soon!
π NeurIPS 2020 Flatland Winners
Almost 4 years agoπ NeurIPS 2020 Flatland Winners
Almost 4 years agoπ NeurIPS 2020 Flatland Winners
Almost 4 years agoπ NeurIPS 2020 Flatland Winners
Almost 4 years agoRound 2 has finished, concluding the NeurIPS 2020 edition!
We have received over 2β000 submissions from over 700 participants coming from over 51 countries! We have been thrilled by your contributions and enthusiasm.
And the winners areβ¦
RL Track
- JBR_HSE, 214.15 points
- ai-team-flatland, 181.497 points
- SSG-RL, 143.985 points
Non-RL Track
- An_Old_Driver, 297.507 points
- MasterFlatland, 296.347 points
- vetrov_andrew, 273.339 points
Congratulations to all of them! The race was intense
Community Prizes
In parallel to the competition, participants also competed for Community Prizes, contributing to the Flatland ecosystem by building tools and implementing ideas that will help other participants.
And the winners are:
- Shivam Agarwal for his tool Visual Analytics of Flatland Episodes
- Samuel LΓ³pez for his Flatland Symphony
- Nilabha Bhattacharya, ex aequo, for his RLlib Colab Notebooks
- Adrian Egli, ex aequo, for his FastTreeObs observation builder
The submissions were few, but they were very impressive and inspiring. We warmly congratulate all four winners.
Whatβs next?
This competition may be finished, but Flatland is just getting started!
We are happy to announce that not only will we apply for a new competition at NeurIPS next year, but we are also organizing another challenge before that in the context of the AMLD 2021 conference, which will take place in Lausanne, Switzerland.
Beyond competitions, Flatland will also become a long-term benchmark, accepting submissions on a permanent basis. Our goal is to measure the progress around the real-world problem of train scheduling, and progress doesnβt happen only twice a year!
For this purpose, a new βpost-challengeβ round will be open soon and will stay open until next competition to allow the community to keep searching.
Thank you all again for your participation, and see you soon on the next leaderboard!
The official results will be announced at NeurIPS
Almost 4 years agoHello everyone,
The review process has been long and complex!
We will ultimately present the official results during the NeurIPS conference, as part of the competition track.
π 5 days left! Final tips & tricks for the home stretch
Almost 4 years agoHey @harshadkhadilkar, the review process took more time than expected, as it was challenging to establish if each solution was using RL in a meaningful way.
We will announce the official results at NeurIPS.
Will the leaderboard remain open?
About 4 years agoHello @guillaume_alleon,
Yes we plan to keep the submissions open! For now new submissions will appear under βpost-challenge submissionsβ (go to βLeaderboard filtersβ -> βInclude post-challenge submissionsβ to show them).
We will also announce the launch of the new Flatland event at NeurIPS
This challenge has raised a lot of interest from both participants and partners, the adventure is just getting started!
Cheers,
Florian
Submission error: weird error during conda clean
About 4 years agoThis is probably related to this: Build problem with the current `environment.yml` file
Make sure your requirements.yml
only contains whatβs strictly necessary!
π 5 days left! Final tips & tricks for the home stretch
About 4 years agoItβs (a): you can submit until the deadline!
Different CUDA version
About 4 years agoBut, as the evaluation environment doesnβt include a GPU, do you really need to setup CUDA in your submissions?
Flatland-Visualization: Visual Analytics of Flatland Episodes
About 4 years agoWow, amazing work
Itβs not clear to me how to create new regions: it seems that I need to select multiple cells by keeping my mouse pressed on the Map View? I donβt see the gray overlay on the Map View or the edges between the vertices on the Graph View
Evaluation took too long
About 4 years agoHello,
How do I deal with the fact that my 8h of evaluation time ran out and the percentage complete is still > 0.25
This is fine, this means your submission stopped because it reached the overall time limit instead of the βpercentage doneβ limit. Most OR submissions reach the overall timeout (you can see on the leaderboard that their mean β% Doneβ is very high).
An early submit on the remote client results in an error and doing nothing also results in an error : Timeout : Evaluation took too long.
I donβt understand what you mean?
About the π Flatland Workshop category
Over 3 years ago(Replace this first paragraph with a brief description of your new category. This guidance will appear in the category selection area, so try to keep it below 200 characters.)
Use the following paragraphs for a longer description, or to establish category guidelines or rules:
Why should people use this category? What is it for?
How exactly is this different than the other categories we already have?
What should topics in this category generally contain?
Do we need this category? Can we merge with another category, or subcategory?