luxai

Gather the most resources and survive the night!

https://www.kaggle.com/c/lux-ai-2021

Solution presentation and description

Google slides

Kaggle post

Methodology

I'm following CRISP-DM 1.0 methodology for the reports.

I have skipped Evaluation and Deployment steps because they are not usually done on Kaggle.

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Solution summary
6. Winning model documentation

• Challenge workflow

Code structure

 |_ luxai: library with code for the challenge
 |_ forum: all the scripts and notebooks taken from the forum
 |_ notebooks: jupyter notebooks made during the challenge. They start by number for easier sorting.
 |_ reports: documents made during the challenge according to CRISP-DM methodology
 |_ tests: folder with tests for the library
 |_ data: folder with light data from teh challenge
 |_ rules: the official rules of the challenge
 |_ agents: folder with agents for the challenge
 |_ scripts: scripts for training agents, playing the game...

Hardware used during the challenge

My PC is described here. It has two RTX3090 GPUs and the rest of the hardware can be seen below.

How to train a model

1. Select matches to download

I did this with this kaggle public notebook. It simply ranks the submitted agents by score and saves a csv file with the agents exceeding a threshold score. The exact csv file used on the final train can be found here.

2. Download the matches

To download the matches I use Google colab that allows to do all the job online and later I simply have to download a zip file with all the matches.

3. Create train configuration files

We have to split the dataset between train and validation. For validation 10% of the matches from the best leaderboard agent were used.

Please replace TRAIN_FOLDER_PATH with the folder on your computer. Also edit the template.yml file to use paths on your computer.

python scripts/train_imitation_learning/create_multiagent_imitation_learning_training.py scripts/final_solution_files/template.yml TRAIN_FOLDER_PATH 0 scripts/final_solution_files/agent_selection_20211201.csv 1700

4. Train

Run the following command and wait for around one day.

python scripts/train_imitation_learning/train_with_generators.py TRAIN_FOLDER_PATH/train_conf.yml

How to create an agent

Creating an agent is very simple, just run this command giving the output folder for the agent and the path of the trained model.

python scripts/create_cunet_agent/create_cunet_agent.py AGENT_PATH TRAIN_FOLDER_PATH/best_val_loss_model.h5

Additional dependences

Keras Conditioned Unet is used and should be downloaded. Then the environment variable KERAS_CUNET_PATH should be set to the downloaded path.