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--- |
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license: mit |
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tags: |
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- reinforcement-learning |
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- chess |
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- agent |
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- code |
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- tabular-regression |
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- tabular-classification |
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- text-classification |
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- text-generation |
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task_categories: |
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- reinforcement-learning |
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- tabular-classification |
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- text-classification |
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- text-generation |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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<div style=" |
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background: #00FF00; |
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border-left: 4px solid #00FF00; |
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padding: 1.5rem; |
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margin: 2rem 0; |
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font-family: 'Fira Code', 'Courier New', monospace; |
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color: #00FF00; |
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border-radius: 0 8px 8px 0; |
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"> |
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<pre style=" |
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font-size: 6px; |
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line-height: 1.2; |
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margin: 0; |
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overflow-x: auto; |
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color: #00FF00; |
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"> |
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_______ ______ _______ _______ _______ _______ _______ _______ _______ _ |
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|\ /|( ____ \( ___ \ |\ /|( ___ )( ____ \ ( ____ \|\ /|( ____ \( ____ \( ____ \ ( ____ )( \ |
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| ) ( || ( \/| ( ) )( \ / )| ( ) || ( \/ | ( \/| ) ( || ( \/| ( \/| ( \/ | ( )|| ( |
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| | _ | || (__ | (__/ / \ (_) / | | | || (_____ | | | (___) || (__ | (_____ | (_____ | (____)|| | |
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| |( )| || __) | __ ( ) _ ( | | | |(_____ ) | | | ___ || __) (_____ )(_____ ) | __)| | |
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| || || || ( | ( \ \ / ( ) \ | | | | ) | | | | ( ) || ( ) | ) | | (\ ( | | |
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| () () || (____/\| )___) )( / \ )| (___) |/\____) | | (____/\| ) ( || (____/\/\____) |/\____) | | ) \ \__| (____/\ |
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(_______)(_______/|/ \___/ |/ \|(_______)\_______) (_______/|/ \|(_______/\_______)\_______) |/ \__/(_______/ |
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</div> |
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# Chess RL Training Export |
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This dataset was created using the synthetic AI vs AI training app found in /generator/. The app simulates games |
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of chess between 2 web workers in a front-end page to train RL datasets. Download it to train your own similar datasets. |
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This dataset is for testing and *UNDER DEVELOPMENT* |
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## Export Information |
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- Export Date: 2026-01-02T22:48:02.105Z |
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- Total Training Games: 0 |
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- Total Moves: 11 |
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- Training Time: 00:00:12 |
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## Files Included |
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### 1. training_games.json |
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Complete dataset of all chess games played during training. Each game includes: |
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- Full PGN notation |
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- Move-by-move records |
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- Game result and metadata |
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- Agent parameters for each game |
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### 2. training_games.csv |
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Same data as JSON but in CSV format for easy import into spreadsheets or databases. |
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### 3. black_agent_model.json |
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Black Agent (Policy Network) configuration and statistics: |
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- Neural network architecture |
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- Hyperparameters (learning rate, exploration rate, etc.) |
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- Training statistics (wins, losses, draws) |
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- Model metadata |
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### 4. green_agent_model.json |
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Green Agent (Value Network) configuration and statistics: |
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- Neural network architecture |
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- Hyperparameters |
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- Training statistics |
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- Model metadata |
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### 5. training_statistics.json |
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Overall training summary and statistics including: |
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- Training duration |
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- Win rates for both agents |
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- System information |
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- Export metadata |
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## Training System |
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Generated by ANN Chess RL Trainer v3.0 by webXOS - A web-based reinforcement learning system for chess AI development. |
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## Usage |
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These files can be used to: |
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- Continue training from this point |
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- Analyze the learning progress |
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- Import into other machine learning frameworks |
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- Share with the research community |
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## Notes |
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- All data is in standard JSON/CSV formats |
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- Compatible with Hugging Face datasets |
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- Can be compressed with GZIP, ZSTD, BZ2, LZ4, or LZMA for upload |
