https://github.com/zjeffer/connect4-deep-rl

Deep reinforcement learning algorithm to solve Connect 4, based on AlphaZero
https://github.com/zjeffer/connect4-deep-rl

ai alphazero deep-learning machine-learning reinforcement-learning

Last synced: 6 days ago
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Deep reinforcement learning algorithm to solve Connect 4, based on AlphaZero

• Host: GitHub
• URL: https://github.com/zjeffer/connect4-deep-rl
• Owner: zjeffer
• License: gpl-3.0
• Created: 2022-08-05T17:57:19.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-29T11:11:17.000Z (4 months ago)
• Last Synced: 2024-10-11T02:53:03.983Z (28 days ago)
• Topics: ai, alphazero, deep-learning, machine-learning, reinforcement-learning
• Language: C++
• Homepage:
• Size: 969 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# AlphaZero on a Connect 4 environment

A deep reinforcement learning algorithm that plays Connect 4, based on AlphaZero. I'm creating this because [my chess algorithm](https://github.com/zjeffer/chess-deep-rl-cpp) learns too slowly, and I wanted to know if the problem is the amount of data needed, or my implementation of the algorithm itself.

See https://zjeffer.github.io/connect4-deep-rl/ for Doxygen documentation.

## TODO

* [X] Connect 4 environment

* [X] MCTS algorithm

* [X] Neural network

* [X] AlphaZero self-play

* [X] Argument parsing

* [ ] Load settings from file

* [X] Unit tests:

  * [X] Horizontal win

  * [X] Vertical win

  * [X] Diagonal win

  * [X] Easy puzzle

  * [ ] Harder puzzle

  * [ ] ...?

* [X] Save played moves to memory, and memory to file

* [X] AlphaZero training

* [ ] AlphaZero evaluation

* [ ] Automatic pipeline for selfplay, training and evaluation

* [ ] Play against computer

* [ ] GUI?

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