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Amazon KDD Cup 2022
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ASCII-rendered single-player dungeon crawl game
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Improving the HTR output of Greek papyri and Byzantine manuscripts
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Machine Learning for detection of early onset of Alzheimers
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See Allgraded | 140046 | ||
graded | 137355 | ||
graded | 137339 |
Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments
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Self-driving RL on DeepRacer cars - From simulation to real world
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Robustness and teamwork in a massively multiagent environment
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See Allfailed | 144468 | ||
graded | 144444 | ||
graded | 144327 |
Multi-Agent Reinforcement Learning on Trains
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See Allfailed | 93962 | ||
graded | 89220 | ||
graded | 89210 |
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See Allgraded | 144152 |
Classify images of snake species from around the world
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See Allgraded | 9963 | ||
failed | 9876 | ||
failed | 9775 |
5 Problems 15 Days. Can you solve it all?
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Disentanglement: from simulation to real-world
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Sample-efficient reinforcement learning in Minecraft
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Multi Agent Reinforcement Learning on Trains.
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See Allfailed | 32805 | ||
failed | 32778 | ||
failed | 32758 |
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See Allgraded | 60255 | ||
failed | 60250 |
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See Allfailed | 60273 | ||
graded | 60271 |
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See Allgraded | 60285 |
Project 2: Road extraction from satellite images
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Project 2: build our own text classifier system, and test its performance.
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Robots that learn to interact with the environment autonomously
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Multi-Agent Reinforcement Learning on Trains
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Participant | Rating |
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hagrid67 | 103 |
Participant | Rating |
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hagrid67 | 103 |
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inside-job FlatlandView
Learn-to-Race: Autonomous Racing Virtual Challenge
Flatland
Current status of imitation agent in baseline repository
About 4 years agoThe above script was for doing PPO and IL alternatelyβ¦
If you want a pure IL, you can try
train.py -ef baselines/custom_imitation_learning_rllib_tree_obs/pure_imitation_tree_obs.yaml --eager --trace
Current status of imitation agent in baseline repository
About 4 years agoThe imitation trainer works. We have generated results for them. You could do a training and simultaneous evaluation using the script
train.py -ief baselines/custom_imitation_learning_rllib_tree_obs/ppo_imitation_tree_obs.yaml --eager --trace
(drop -e
flag if you donβt want to do evaluation)
The only thing is the OR expert solution uses was for an older flatland version where the malfunction rate was different. So if you are training with malfunctions, you can workaround it by doing the below changes in the flatland source code
change below line in method malfunction_from_file in the file flatland.envs.malfunction_generators.py
mean_malfunction_rate = 1/oMPD.malfunction_rate
The documentation here https://flatland.aicrowd.com/research/baselines/imitation_learning.html is a bit old , we will update it soon.
You can refer to this Google Colab notebook also which has the details along with the results https://colab.research.google.com/drive/1oK8yaTSVYH4Av_NwmhEC9ZNBS_Wwhi18#scrollTo=P_IMrdL27Ii7
Let me know if you are facing any issues.
RLLib Baselines on Colab!
About 4 years agoWe have taken the repo from https://gitlab.aicrowd.com/flatland/neurips2020-flatland-baselines
and made it into a simple colab notebook
All training scripts are also provided, so one can modify the configs and do runs of their own. Evaluation is also run and the script to calculate scores on an independent test set is also provided.
Using a trained agent in RLlib
About 4 years agoYour approach seems correct in principle β¦ not sure why the trainer cannot restore from checkpoint. You could compare with the example provided.
Using a trained agent in RLlib
About 4 years agoyou can refer to the rollout.py
script in the AIcrowd baselines for flatland
And the corresponding script
Note that this runs small environments with a custom seed. You will have to change the environment logic for your purpose.
Expert demonstrations for Imitation Learning: Recreating Malfunctions
About 4 years agoThe flatland-rl version has been updated to 2.2.2. (Upgrade it using the command pip install -U flatland-rl
). Can you check if the malfunctions are replicable with the same seed? Let us know if you are facing any issues.
Expert demonstrations for Imitation Learning: Recreating Malfunctions
About 4 years agoIt does it slightly different from the MARWIL/Apex-DQfD versions in that it runs every episode alternatively via IL and RL (the ratio is defaulted to 50% ratio but it can be changed and also decayed over time by changing the configs).
Expert demonstrations for Imitation Learning: Recreating Malfunctions
About 4 years agoYou could also try our online RL Solution which does not require any of these intermediate steps like generating experiences. It runs everything on the flyβ¦
You can find a pure IL and IL + PPO solution here
We havenβt documented it but we will do it soon. It uses the last yearβs 2nd place solution from CkUA. Unfortunately, it was from an earlier flatland version, so as of now you have to change the malfunction behaviour as per previous versions as follows
change below line in method malfunction_from_file in the file flatland.envs.malfunction_generators.py
mean_malfunction_rate = 1/oMPD.malfunction_rate
Expert demonstrations for Imitation Learning: Recreating Malfunctions
About 4 years agoAre you using the same flatland versions for both creation and loading environments? The solution for creating the experiences in the AICrowd baselines for MARWIL and APE-X DQfD were mostly used in environments without malfunctions and they used the seed value of 1001 (https://flatland.aicrowd.com/research/baselines/imitation_learning.html).
Flatland Challenge
Solution Codes and Approaches
Almost 5 years agoI havenβt submitted it yet. But I can share the results of few envs from the local evaluation
Evaluation Number : 1
Reward : -43.83333333333334
====================================================================================================
Evaluation Number : 1
Current Env Path : ./test-envs/Test_5/Level_1.pkl
Env Creation Time : 0.42258167266845703
Number of Steps : 1120
Mean/Std of Time taken by Controller : 0.01895513470683779 0.0018288652394336735
Mean/Std of Time per Step : 0.1480530451451029 0.00961317459096298
Evaluation Number : 2
Reward : -43.41666666666668
====================================================================================================
Evaluation Number : 2
Current Env Path : ./test-envs/Test_3/Level_0.pkl
Env Creation Time : 0.29577183723449707
Number of Steps : 960
Mean/Std of Time taken by Controller : 0.02316151708364487 0.002471594250438426
Mean/Std of Time per Step : 0.14834324022134146 0.01293447105111391
Evaluation Number : 3
Reward : -52.00000000000002
====================================================================================================
Evaluation Number : 3
Current Env Path : ./test-envs/Test_6/Level_0.pkl
Env Creation Time : 1.5495717525482178
Number of Steps : 1760
Mean/Std of Time taken by Controller : 0.02436668398705396 0.0039495335690074304
Mean/Std of Time per Step : 0.19943869560956956 0.024274925544436107
Solution Codes and Approaches
Almost 5 years agoI have added another code file with a different approach that does not use a model.
The code can be found in the local Github location
For a simple demonstration of how we solve a dense railway network, simply run the file
MultipleAgentNavigationObsConflict.py.
This file does not use any additional packages other than the ones required for flatland and can be run with the latest flatland-rl version 2.1.10
Solution Codes and Approaches
Almost 5 years agoI have put up some code here
This includes actorcritictrainer.py file which implements an actor critic approach and ESStrategyTraining.py which implements an evolutionary strategy approach.
The results seem to be similar to the Duelling Double DQN approach. I have saved sample results and pre-trained weights.
This has been done using stock observations.
Adding to Erikβs comments, my observations are
- These models do not show improvement even after training for longer periods and show comparable performance, suggesting that we need to do better feature engineering.
As of now, I next plan to do some visualizations and add documentation to the code to better.
Any comments/suggestions are most welcome.
Submission Errors Flatland
Over 5 years agoI am getting an error on evaluation
https://gitlab.aicrowd.com/nilabha/flatland-challenge-starter-kit/issues/6
Can yo help with the logs
Submission Errors Flatland
Over 5 years agoI am not able to see logs but I can see comments on the issue though only the first line.
2019-08-03T15:15:13.396985671Z Traceback (most recent call last):β¦
Submission Errors Flatland
Over 5 years agoI am getting an error in evaluation
https://gitlab.aicrowd.com/nilabha/flatland-challenge-starter-kit/issues/3
Somehow I cannot see any error logs though debug=True
I have tested this in the local environment (using redis server etcβ¦) and it is working.
Can you please help with the error logs.
Thanks,
Nilabha
SnakeCLEF2021 - Snake Species Identification Chall
Submission Errors
Over 5 years agoThanks kongas
I used the below code to remove the images
filter_func = lambda x: str(x) not in lsRemove
test_img = (ImageList.from_folder(path).filter_by_func(filter_func))
Though there is another errorβ¦
https://gitlab.aicrowd.com/nilabha/snake-species-identification-challenge/issues/33
@mohanty
Has the competition ended or will it restart again. Would have liked to get a score as my validation results were good.
Is it possible to get the error logs?
Evaluation stuck [Edit : Evaluation took a long time]
Over 5 years agoall submissions seems to be queued for a long time.
Is there some problem?
Thanks
Submission Errors
Over 5 years ago@mohanty
I have put a workaround to find the images which fail loading and delete them and later add these probability which are all equal to 1/45
However I still get an error
https://gitlab.aicrowd.com/nilabha/snake-species-identification-challenge/issues/32
Can you please help with the error?
Thanks,
Nilabha
Notebooks
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F1:0.376 Image pixel representation + CNN Model Baseline Create Image-Pixel like representation features and Convolution Neural Network based baselinenilabhaΒ· Over 3 years ago
-
F1:0.52-Baseline Imbalance Samplers(20+) and 8Classifiers Automated Benchmark of Imbalanced Samplers and Classifiers + Feature Engineering with Shapley ValuesnilabhaΒ· Over 3 years ago
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RLLib Baselines on Colab! This Colab notebook allows you to train a full Flatland agent using the provided PPO baseline.nilabhaΒ· Almost 4 years ago
ποΈ Claim Your Training Credits
Over 2 years agoSubmission Id : 174025
Work in Finance Domain as a day job and experienced in RL across a range of projects and competitions.