https://github.com/goodcoder666/katac4
An AlphaZero engine for Saiblo Connect4, featuring a pure Python implementation of key KataGo techniques.
https://github.com/goodcoder666/katac4
alphazero board-game connect-four connect4-ai katago mcts monte-carlo-tree-search pytorch reinforcement-learning rl torchscript
Last synced: about 2 months ago
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An AlphaZero engine for Saiblo Connect4, featuring a pure Python implementation of key KataGo techniques.
- Host: GitHub
- URL: https://github.com/goodcoder666/katac4
- Owner: GoodCoder666
- License: mit
- Created: 2025-06-07T12:56:02.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-10T13:52:50.000Z (4 months ago)
- Last Synced: 2025-07-03T22:35:27.806Z (3 months ago)
- Topics: alphazero, board-game, connect-four, connect4-ai, katago, mcts, monte-carlo-tree-search, pytorch, reinforcement-learning, rl, torchscript
- Language: Python
- Homepage: https://goodcoder666.github.io/p/katac4/
- Size: 2.09 MB
- Stars: 8
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# katac4
An AlphaZero engine for [Saiblo Connect4](https://www.saiblo.net/game/3), featuring a pure Python implementation of key [KataGo](https://github.com/lightvector/KataGo) techniques.
## Project Structure
```
katac4/
├── README.md # This file
│
├── runs/ # TensorBoard logs
├── weights/ # Model checkpoints for each training run
│
├── docs/ # Documentation files
│ └── methods.md # Overview of improvements over original AlphaZero
│
├── saiblo/ # Saiblo Connect4 submission package
│ ├── main.py # Entry point
│ ├── game.py # Standalone game environment (with zobrist hash)
│ └── search.py # Monte Carlo Graph Search, used during online play
│
├── train.py # Main training script
├── model.py # Neural network model definition (b3c128nbt)
├── game.py # Game environment
├── mcts.py # Monte Carlo Tree Search, used during training
├── elo_eval.py # Script for evaluating model ELO ratings
├── elo.json # ELO ratings generated by elo_eval.py
├── elo_plot.py # Plots ELO rating progression
├── export_model.py # Converts model to TorchScript for deployment
├── benchmark.py # Benchmarks model performance
└── human_play.py # Allows human interaction with the model
```
## Quick Start
### Self-Play Training
Before starting self-play training, you may want to adjust a few parameters in `train.py`:
* `epochs`: Total number of training epochs
* `epoch_size`: Number of self-play games per epoch
* `parallel_games`: Number of games run in parallel
* `num_gpus`: Number of GPUs to utilize
> [!IMPORTANT]
>
> To exactly reproduce the most recent training run, **do not change** the default parameter values in the code.
To begin training, run:
```bash
python3 train.py
```
Model checkpoints will be saved to the `weights/` directory. To monitor progress with TensorBoard:
```bash
tensorboard --logdir runs
```
### ELO Evaluation
To evaluate model performance using ELO, edit the `ai_list` and `weight_format` variables in `elo_eval.py` as needed. Then run:
```bash
python3 elo_eval.py
```
Models will compete in randomized pairings, with results saved to `elo.json`. To visualize the rating progression:
```bash
python3 elo_plot.py
```
> [!NOTE]
>
> The ELO evaluation process is computationally intensive. For reference, the most recent evaluation was based on approximately 70,000 games and required 2.5 days to complete on four RTX 4090 GPUs.
### Submitting to Saiblo
Select your best-performing model checkpoint (typically the final one or the one with highest ELO), and set its path in `export_model.py`. Export it to TorchScript format by running:
```bash
python3 export_model.py
```
This will generate `model.pt` in the `saiblo/` directory. To create your submission, zip **only the files** inside the `saiblo/` folder—**do not include the folder itself** in the archive.