My published benchmark for a Kaggle Simulations Competition

Last update: Aug 22, 2022

Overview

Lux AI Working Title Bot

Please refer to the Kaggle notebook for the comment section. The comment section contains my explanation on my code structure and logic.

This README will explain how to use this repository.

Setup

Run ./setup.sh to install the required components.

Make submission files

Run ./make_submit.sh to generate the following files

The zipfile submission.tar.gz which can be submitted to Kaggle directly.
The notebook submission notebook_generated.ipynb. You can upload this notebook into a fork of my notebook and submit.

Running a Game

You can run a game with notebook_test.ipynb

You may define the width and height of the board, as well as the seed. Annotations are shown on the board.

This code snippet is also run in the generated notebook.

Analysing Game State and Missions

You may run the game logic on a specific turn with notebook_debug.ipynb to understand why the agent made a certain decision.

When the run is made with notebook_test.ipynb, the game state, and missions are saved as Python pickle files. You may load the Python pickle file and run the game logic to produce the actions and updated missions.

As the game logic is run, it prints the new mission planned, the actions made include the annotation.

You can modify the code and see how the agent reacts different for the same game state and missions. This allows you iteratively improve the agent more quicking.

This code snippet is also run in the generated notebook.

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Comments

Maybe a bug.
https://github.com/tonghuikang/lux-ai-2021/blob/86d06bb0ae799bd66f732052597fd67d2d91cbf0/lux/game.py#L447

When reading your sharing code for lux-ai-2021, I found there may be a bug.

In file game.py , the function calculate_distance_matrix() near line 447, I think it should be:

x_distance_from_edge = min(x, self.map_width-x-1)

Since we are calculating the distance from edge here, that's to say we need y-to-edge and x-to-edge.

Maybe I am wrong and not get the key point.
And thanks for your sharing : )
opened by floudk 2

Releases(rules-final)

rules-final(Dec 19, 2021)

Versions larger than this uses a neural network model trained by imitation.
Source code(tar.gz)
Source code(zip)
v5(Oct 1, 2021)

See https://www.kaggle.com/huikang/lux-ai-working-title-bot/comments#1516212 for comments on Version 5 changes.
Source code(tar.gz)
Source code(zip)
v4(Sep 17, 2021)

See https://www.kaggle.com/huikang/lux-ai-working-title-bot/comments#1516212 for comments on Version 4 changes.
Source code(tar.gz)
Source code(zip)
v3(Sep 4, 2021)

See https://www.kaggle.com/huikang/lux-ai-working-title-bot/comments?scriptVersionId=73922852#1502332 for comments on Version 3 changes.
Source code(tar.gz)
Source code(zip)
v2(Aug 31, 2021)

See https://www.kaggle.com/huikang/lux-ai-working-title-bot/comments?scriptVersionId=73552476#1497289 for comments on Version 2 changes.
Source code(tar.gz)
Source code(zip)

Owner

Tong Hui Kang

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GitHub Repository https://kaggle.com/c/lux-ai-2021/

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