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"
}
|
Chess RL Training Export
This dataset was created using 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. This dataset is for testing and UNDER DEVELOPMENT as we attempt to enhance and make more datasets using this app.
Export Information
- Export Date: 2026-01-02T22:48:02.105Z
- Total Training Games: 0
- Total Moves: 11
- Training Time: 00:00:12
Files Included
1. training_games.json
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
2. training_games.csv
Same data as JSON but in CSV format for easy import into spreadsheets or databases.
3. 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
4. green_agent_model.json
Green Agent (Value Network) configuration and statistics:
- Neural network architecture
- Hyperparameters
- Training statistics
- Model metadata
5. training_statistics.json
Overall training summary and statistics including:
- Training duration
- Win rates for both agents
- System information
- Export metadata
Training System
Generated by ANN Chess RL Trainer v3.0 - A web-based reinforcement learning system for chess AI development.
Usage
These files can be used to:
- Continue training from this point
- Analyze the learning progress
- Import into other machine learning frameworks
- Share with the research community
Notes
- 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
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