training_summary dict | agent_comparison dict | export_timestamp stringclasses 1
value | system_info dict |
|---|---|---|---|
{
"total_games": 0,
"total_moves": 11,
"training_time": "00:00:12",
"current_game": 2,
"training_active": false
} | {
"black_win_rate": "0.0",
"green_win_rate": "0.0",
"draws_rate": "0.0"
} | 2026-01-02T22:48:02.103Z | {
"user_agent": "Mozilla/5.0 (X11; Linux x86_64; rv:146.0) Gecko/20100101 Firefox/146.0",
"platform": "Linux x86_64",
"screen_resolution": "1920x1080"
} |
👁 Website
👁 GitHub
👁 Hugging Face
👁 Follow on X
_______ ______ _______ _______ _______ _______ _______ _______ _______ _ |\ /|( ____ \( ___ \ |\ /|( ___ )( ____ \ ( ____ \|\ /|( ____ \( ____ \( ____ \ ( ____ )( \ | ) ( || ( \/| ( ) )( \ / )| ( ) || ( \/ | ( \/| ) ( || ( \/| ( \/| ( \/ | ( )|| ( | | _ | || (__ | (__/ / \ (_) / | | | || (_____ | | | (___) || (__ | (_____ | (_____ | (____)|| | | |( )| || __) | __ ( ) _ ( | | | |(_____ ) | | | ___ || __) (_____ )(_____ ) | __)| | | || || || ( | ( \ \ / ( ) \ | | | | ) | | | | ( ) || ( ) | ) | | (\ ( | | | () () || (____/\| )___) )( / \ )| (___) |/\____) | | (____/\| ) ( || (____/\/\____) |/\____) | | ) \ \__| (____/\ (_______)(_______/|/ \___/ |/ \|(_______)\_______) (_______/|/ \|(_______/\_______)\_______) |/ \__/(_______/
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
- Downloads last month
- 24