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README.md

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+# Chess RL Training Export
+
+## 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

black_agent_model.json

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+{
+  "model_type": "chess_policy_network",
+  "color": "black",
+  "architecture": {
+    "input_size": 896,
+    "hidden_layers": [
+      128,
+      64,
+      32
+    ],
+    "output_size": 1,
+    "activation": [
+      "relu",
+      "relu",
+      "relu",
+      "tanh"
+    ],
+    "dropout": 0.2
+  },
+  "hyperparameters": {
+    "learning_rate": 0.001,
+    "exploration_rate": 0.3,
+    "discount_factor": 0.95
+  },
+  "training_stats": {
+    "wins": 0,
+    "losses": 0,
+    "draws": 0,
+    "total_games": 0,
+    "moves_made": 5
+  },
+  "export_timestamp": "2026-01-02T22:48:02.101Z"
+}

green_agent_model.json

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+{
+  "model_type": "chess_value_network",
+  "color": "green",
+  "architecture": {
+    "input_size": 896,
+    "hidden_layers": [
+      128,
+      64,
+      32
+    ],
+    "output_size": 1,
+    "activation": [
+      "relu",
+      "relu",
+      "relu",
+      "tanh"
+    ],
+    "dropout": 0.2
+  },
+  "hyperparameters": {
+    "learning_rate": 0.001,
+    "exploration_rate": 0.3,
+    "discount_factor": 0.95
+  },
+  "training_stats": {
+    "wins": 0,
+    "losses": 0,
+    "draws": 0,
+    "total_games": 0,
+    "moves_made": 6
+  },
+  "export_timestamp": "2026-01-02T22:48:02.101Z"
+}

training_statistics.json

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+{
+  "training_summary": {
+    "total_games": 0,
+    "total_moves": 11,
+    "training_time": "00:00:12",
+    "current_game": 2,
+    "training_active": false
+  },
+  "agent_comparison": {
+    "black_win_rate": "0.0",
+    "green_win_rate": "0.0",
+    "draws_rate": "0.0"
+  },
+  "export_timestamp": "2026-01-02T22:48:02.103Z",
+  "system_info": {
+    "user_agent": "Mozilla/5.0 (X11; Linux x86_64; rv:146.0) Gecko/20100101 Firefox/146.0",
+    "platform": "Linux x86_64",
+    "screen_resolution": "1920x1080"
+  }
+}