| --- |
| 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=" |
| background: #00FF00; |
| border-left: 4px solid #00FF00; |
| padding: 1.5rem; |
| margin: 2rem 0; |
| font-family: 'Fira Code', 'Courier New', monospace; |
| color: #00FF00; |
| border-radius: 0 8px 8px 0; |
| "> |
| <pre style=" |
| font-size: 6px; |
| line-height: 1.2; |
| margin: 0; |
| overflow-x: auto; |
| color: #00FF00; |
| "> |
| _______ ______ _______ _______ _______ _______ _______ _______ _______ _ |
| |\ /|( ____ \( ___ \ |\ /|( ___ )( ____ \ ( ____ \|\ /|( ____ \( ____ \( ____ \ ( ____ )( \ |
| | ) ( || ( \/| ( ) )( \ / )| ( ) || ( \/ | ( \/| ) ( || ( \/| ( \/| ( \/ | ( )|| ( |
| | | _ | || (__ | (__/ / \ (_) / | | | || (_____ | | | (___) || (__ | (_____ | (_____ | (____)|| | |
| | |( )| || __) | __ ( ) _ ( | | | |(_____ ) | | | ___ || __) (_____ )(_____ ) | __)| | |
| | || || || ( | ( \ \ / ( ) \ | | | | ) | | | | ( ) || ( ) | ) | | (\ ( | | |
| | () () || (____/\| )___) )( / \ )| (___) |/\____) | | (____/\| ) ( || (____/\/\____) |/\____) | | ) \ \__| (____/\ |
| (_______)(_______/|/ \___/ |/ \|(_______)\_______) (_______/|/ \|(_______/\_______)\_______) |/ \__/(_______/ |
| |
| |
| </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 |
