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license: mit
tags:
- reinforcement-learning
- chess
- agent
- code
- tabular-regression
- tabular-classification
- text-classification
- text-generation
task_categories:
- reinforcement-learning
- tabular-classification
- text-classification
- text-generation
---
[](https://webxos.netlify.app)
[](https://github.com/webxos/webxos)
[](https://huggingface.co/webxos)
[](https://x.com/webxos)
<div style="
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border-left: 4px solid #00FF00;
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margin: 2rem 0;
font-family: 'Fira Code', 'Courier New', monospace;
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<pre style="
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_______ ______ _______ _______ _______ _______ _______ _______ _______ _
|\ /|( ____ \( ___ \ |\ /|( ___ )( ____ \ ( ____ \|\ /|( ____ \( ____ \( ____ \ ( ____ )( \
| ) ( || ( \/| ( ) )( \ / )| ( ) || ( \/ | ( \/| ) ( || ( \/| ( \/| ( \/ | ( )|| (
| | _ | || (__ | (__/ / \ (_) / | | | || (_____ | | | (___) || (__ | (_____ | (_____ | (____)|| |
| |( )| || __) | __ ( ) _ ( | | | |(_____ ) | | | ___ || __) (_____ )(_____ ) | __)| |
| || || || ( | ( \ \ / ( ) \ | | | | ) | | | | ( ) || ( ) | ) | | (\ ( | |
| () () || (____/\| )___) )( / \ )| (___) |/\____) | | (____/\| ) ( || (____/\/\____) |/\____) | | ) \ \__| (____/\
(_______)(_______/|/ \___/ |/ \|(_______)\_______) (_______/|/ \|(_______/\_______)\_______) |/ \__/(_______/
</div>
# Chess RL Training Export
This dataset was created using the synthetic AI vs AI training app found in /generator/. The app simulates games
of chess between 2 web workers in a front-end page to train RL datasets. Download it to train your own similar datasets.
- Export Date: 2026-01-02T22:48:02.105Z
- Total Training Games: 0
- Total Moves: 11
- Training Time: 00:00:12
Complete dataset of all chess games played during training. Each game includes:
- Full PGN notation
- Move-by-move records
- Game result and metadata
- Agent parameters for each game
### training_games.csv
Same data as JSON but in CSV format for easy import into spreadsheets or databases.
### black_agent_model.json
Black Agent (Policy Network) configuration and statistics:
- Neural network architecture
- Hyperparameters (learning rate, exploration rate, etc.)
- Training statistics (wins, losses, draws)
- Model metadata
### green_agent_model.json
Green Agent (Value Network) configuration and statistics:
- Neural network architecture
- Hyperparameters
- Training statistics
- Model metadata
### training_statistics.json
Overall training summary and statistics including:
- Training duration
- Win rates for both agents
- System information
- Export metadata
## Usage
- Continue training from this point
- Analyze the learning progress
- Import into other machine learning frameworks
- Share with the research community
- All data is in standard JSON/CSV formats
- Compatible with Hugging Face datasets
- Can be compressed with GZIP, ZSTD, BZ2, LZ4, or LZMA for upload |