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nilabha
Nilabha Bhattacharya

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Bangalore, IN

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Challenges Entered

ESCI Challenge for Improving Product Search
By Amazon Search

Amazon KDD Cup 2022

Latest submissions

No submissions made in this challenge.
NeurIPS 2021 - The NetHack Challenge
By AIcrowd

ASCII-rendered single-player dungeon crawl game

Latest submissions

No submissions made in this challenge.
IJCAI 2022 - The Neural MMO Challenge
By Parametrix.ai MIT THU_SIGS AIcrowd

Latest submissions

No submissions made in this challenge.

Improving the HTR output of Greek papyri and Byzantine manuscripts

Latest submissions

No submissions made in this challenge.

Multi-Agent RL for Trains

Latest submissions

 failed 156823 Fri, 17 Sep 2021 12:57:58 graded 154860 Tue, 7 Sep 2021 06:14:59 graded 154780 Mon, 6 Sep 2021 17:45:14

Machine Learning for detection of early onset of Alzheimers

Latest submissions

 graded 140046 Sat, 22 May 2021 13:41:02 graded 137355 Fri, 14 May 2021 18:36:46 graded 137339 Fri, 14 May 2021 17:21:55
Learn-to-Race: Autonomous Racing Virtual Challenge
By Carnegie Mellon University Arrival

The first, open autonomous racing challenge.

Latest submissions

 graded 174025 Wed, 16 Feb 2022 14:54:52
NeurIPS 2020: Procgen Competition
By OpenAI

Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments

Latest submissions

No submissions made in this challenge.
NeurIPS 2021 AWS DeepRacer AI Driving Olympics Challenge
By AIcrowd

Self-driving RL on DeepRacer cars - From simulation to real world

Latest submissions

No submissions made in this challenge.
The Neural-MMO Challenge
By MIT

Robustness and teamwork in a massively multiagent environment

Latest submissions

No submissions made in this challenge.
Music Demixing Challenge ISMIR 2021
By Sony Group Corporation

Latest submissions

 failed 144468 Fri, 4 Jun 2021 20:13:57 graded 144444 Fri, 4 Jun 2021 18:07:47 graded 144327 Fri, 4 Jun 2021 10:49:52
Flatland
By SNCF SBB Deutsche Bahn

Multi-Agent Reinforcement Learning on Trains

Latest submissions

 failed 93962 Sat, 31 Oct 2020 07:43:59 graded 89220 Sun, 18 Oct 2020 06:29:06 graded 89210 Sun, 18 Oct 2020 05:59:12

Latest submissions

 graded 144152 Thu, 3 Jun 2021 18:21:44
SnakeCLEF2021 - Snake Species Identification Challenge
By LifeCLEF Institute of Global Health

Classify images of snake species from around the world

Latest submissions

 graded 9963 Tue, 30 Jul 2019 13:23:40 failed 9876 Mon, 29 Jul 2019 21:03:51 failed 9775 Sun, 28 Jul 2019 11:48:47
NeurIPS 2019: Learn to Move - Walk Around
By Stanford Neuromuscular Biomechanics Laboratory

Reinforcement Learning on Musculoskeletal Models

Latest submissions

 graded 9744 Sat, 27 Jul 2019 19:23:31 graded 9743 Sat, 27 Jul 2019 19:18:54
AIcrowd Blitz⚡#2
By AIcrowd

5 Problems 15 Days. Can you solve it all?

Latest submissions

No submissions made in this challenge.
NeurIPS 2019 : Disentanglement Challenge
By Max Planck Institute for Intelligent Systems

Disentanglement: from simulation to real-world

Latest submissions

No submissions made in this challenge.
NeurIPS 2019 : MineRL Competition
By MineRL Labs - Carnegie Mellon University

Sample-efficient reinforcement learning in Minecraft

Latest submissions

No submissions made in this challenge.
Flatland Challenge
By SBB

Multi Agent Reinforcement Learning on Trains.

Latest submissions

 failed 32805 Sun, 5 Jan 2020 10:33:45 failed 32778 Sun, 5 Jan 2020 04:46:05 failed 32758 Sat, 4 Jan 2020 22:08:30
MNIST
By AIcrowd

Recognise Handwritten Digits

Latest submissions

 graded 60255 Fri, 3 Apr 2020 13:37:09 failed 60250 Fri, 3 Apr 2020 13:26:28
OLNWP
By AIcrowd

Online News Prediction

Latest submissions

 failed 60273 Fri, 3 Apr 2020 14:06:41 graded 60271 Fri, 3 Apr 2020 14:03:08
CRDSM
By AIcrowd KAIR

Crowdsourced Map Land Cover Prediction

Latest submissions

 graded 60285 Fri, 3 Apr 2020 14:24:43
By EPFL ML

Project 2: Road extraction from satellite images

Latest submissions

No submissions made in this challenge.
EPFL ML Text Classification
By EPFL ML

Project 2: build our own text classifier system, and test its performance.

Latest submissions

No submissions made in this challenge.
NeurIPS 2019 - Robot open-Ended Autonomous Learning
By GOAL-Robots

Robots that learn to interact with the environment autonomously

Latest submissions

No submissions made in this challenge.
Flatland AMLD 2021
By AIcrowd

Multi-Agent Reinforcement Learning on Trains

Latest submissions

No submissions made in this challenge.
Participant Rating
hagrid67 103
Participant Rating
hagrid67 103

Learn-to-Race: Autonomous Racing Virtual Challenge

Submission Id : 174025
Work in Finance Domain as a day job and experienced in RL across a range of projects and competitions.

Current status of imitation agent in baseline repository

Over 2 years ago

The above script was for doing PPO and IL alternately…

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

Over 2 years ago

The 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 don’t 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

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!

Over 2 years ago

We 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

Over 2 years ago

Your approach seems correct in principle … not sure why the trainer cannot restore from checkpoint. You could compare with the example provided.

Using a trained agent in RLlib

Over 2 years ago

you 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.

Over 2 years ago

Expert demonstrations for Imitation Learning: Recreating Malfunctions

Over 2 years ago

The 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

Over 2 years ago

It 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

Over 2 years ago

You could also try our online RL Solution which does not require any of these intermediate steps like generating experiences. It runs everything on the fly…
You can find a pure IL and IL + PPO solution here

We haven’t documented it but we will do it soon. It uses the last year’s 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

mean_malfunction_rate = 1/oMPD.malfunction_rate

Expert demonstrations for Imitation Learning: Recreating Malfunctions

Over 2 years ago

Are 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).

Solution Codes and Approaches

I haven’t submitted it yet. But I can share the results of few envs from the local evaluation

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

I 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
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

Over 3 years ago

I 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.

• 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.

Submission Errors Flatland

Over 3 years ago

I 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 3 years ago

@ashivani

I 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):…

Submission Errors Flatland

Over 3 years ago

@mohanty

I 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 etc…) and it is working.

Thanks,
Nilabha

Submission Errors

Over 3 years ago

Thanks 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 error…
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 3 years ago

@mohanty

all submissions seems to be queued for a long time.
Is there some problem?

Thanks

Submission Errors

Over 3 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