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Connect-Zero: Reinforcement Learning from Scratch

This repository implements Reinforcement Learning (RL) techniques from scratch for the game of Connect 4. It serves as the companion repo to the blog post series about Connect-Zero. Currently it implements:

It also contains some utility scripts for having models play single games or tournaments against each other, perform pretraining, evaluate the performance on tactical puzzles, and export models to ONNX format.

The scripts require torch, matplotlib, numpy, and click.

To run the examples, navigate to the train/ subdirectory and execute e.g.

$ python example3-rwb.py

The webapp/ subdirectory contains a JavaScript applet for interactively playing against an ONNX exported model.

If you want to play against a live version, the strongest public version is currently hosted in the applet in the RwB post.

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Reinforcement Learning from scratch for Connect 4

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