Upload AI Racing Game model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+training_history.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+tags:
+- reinforcement-learning
+- behavioral-cloning
+- racing-game
+- tensorflow
+- neural-network
+language:
+- en
+datasets:
+- synthetic
+metrics:
+- accuracy
+- f1
+library_name: tensorflow
+pipeline_tag: other
+---
+
+# AI Racing Game Model
+
+This is a neural network trained to play a racing game using behavioral cloning. The model learns from expert demonstrations to make driving decisions (left, stay, right) based on the current game state.
+
+## Model Details
+
+- **Model Type**: Feed-forward Neural Network
+- **Framework**: TensorFlow/Keras
+- **Training Method**: Behavioral Cloning (Supervised Learning)
+- **Input**: 9-dimensional state vector
+- **Output**: 3-dimensional action probabilities
+
+## Training Data
+
+- **Total Samples**: 75,000
+- **Data Source**: Synthetic expert demonstrations
+- **Difficulty Levels**: Progressive (0.5x to 1.5x)
+- **Training Method**: Supervised learning on expert actions
+
+## Model Architecture
+
+```
+Input Layer: 9 features
+Hidden Layer 1: 64 neurons (ReLU + BatchNorm + Dropout 0.3)
+Hidden Layer 2: 32 neurons (ReLU + BatchNorm + Dropout 0.2)  
+Hidden Layer 3: 16 neurons (ReLU + Dropout 0.1)
+Output Layer: 3 neurons (Softmax)
+```
+
+## Performance
+
+- **Test Accuracy**: 0.9879
+- **Test Loss**: 0.0471
+- **Weighted F1-Score**: 0.9861
+
+### Per-Class Metrics
+- **Left Action**: Precision: 0.901, Recall: 0.471, F1: 0.618
+- **Stay Action**: Precision: 0.989, Recall: 1.000, F1: 0.994
+- **Right Action**: Precision: 0.973, Recall: 0.545, F1: 0.699
+
+## Usage
+
+### TensorFlow.js (Web)
+```javascript
+// Load the model
+const model = await tf.loadLayersModel('model.json');
+
+// Prepare input (9-dimensional array)
+const gameState = tf.tensor2d([[
+    lane0_near, lane0_far,    // Lane 0 sensors
+    lane1_near, lane1_far,    // Lane 1 sensors  
+    lane2_near, lane2_far,    // Lane 2 sensors
+    current_lane_norm,        // Current lane (0-1)
+    progress_norm,           // Game progress (0-1)
+    speed_factor            // Speed factor
+]], [1, 9]);
+
+// Get prediction
+const prediction = model.predict(gameState);
+const actionProbs = await prediction.data();
+
+// Choose action (0=Left, 1=Stay, 2=Right)
+const action = actionProbs.indexOf(Math.max(...actionProbs));
+```
+
+### Python (TensorFlow)
+```python
+import tensorflow as tf
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model('racing_model.keras')
+
+# Prepare input
+game_state = np.array([[
+    lane0_near, lane0_far,
+    lane1_near, lane1_far, 
+    lane2_near, lane2_far,
+    current_lane_norm,
+    progress_norm,
+    speed_factor
+]])
+
+# Get prediction
+action_probs = model.predict(game_state)[0]
+action = np.argmax(action_probs)  # 0=Left, 1=Stay, 2=Right
+```
+
+## Input Format
+
+The model expects a 9-dimensional input vector:
+1. **lane0_near** (0-1): Near obstacle sensor for left lane
+2. **lane0_far** (0-1): Far obstacle sensor for left lane  
+3. **lane1_near** (0-1): Near obstacle sensor for middle lane
+4. **lane1_far** (0-1): Far obstacle sensor for middle lane
+5. **lane2_near** (0-1): Near obstacle sensor for right lane
+6. **lane2_far** (0-1): Far obstacle sensor for right lane
+7. **current_lane_norm** (0-1): Current lane position normalized
+8. **progress_norm** (0-1): Game progress/score normalized
+9. **speed_factor** (0-1): Current game speed factor
+
+## Output Format
+
+The model outputs 3 probability values:
+- **Index 0**: Probability of moving left
+- **Index 1**: Probability of staying in current lane  
+- **Index 2**: Probability of moving right
+
+## Files Included
+
+- `model.json` + `*.bin`: TensorFlow.js model files
+- `racing_model.keras`: Native Keras model
+- `metadata.json`: Model metadata and training info
+- `training_history.png`: Training progress visualization
+
+## Training Details
+
+- **Epochs**: 30
+- **Batch Size**: 64
+- **Optimizer**: Adam (lr=0.001)
+- **Loss Function**: Categorical Crossentropy
+- **Early Stopping**: Patience 8
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{ai_racing_model,
+  title={AI Racing Game Neural Network},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Relacosm/theline-v1}
+}
+```

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metadata.json

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+{
+  "model_architecture": {
+    "input_shape": [
+      null,
+      9
+    ],
+    "output_shape": [
+      null,
+      3
+    ],
+    "total_params": 3683,
+    "trainable_params": 3491
+  },
+  "training_info": {
+    "final_accuracy": 0.9865882396697998,
+    "final_loss": 0.048811547458171844,
+    "epochs_trained": 30,
+    "best_accuracy": 0.987686276435852,
+    "test_accuracy": 0.9879111051559448,
+    "test_loss": 0.04710409790277481,
+    "training_date": "2025-09-07T19:52:09.037470"
+  },
+  "training_parameters": {
+    "num_samples": 75000,
+    "epochs": 30,
+    "batch_size": 64,
+    "validation_split": 0.2,
+    "test_split": 0.15,
+    "early_stopping_patience": 8,
+    "reduce_lr_patience": 5
+  },
+  "repository_info": {
+    "huggingface_repo": "Relacosm/theline-v1",
+    "wandb_project": "ai-racing-game"
+  },
+  "usage_instructions": {
+    "input_format": "9-dimensional array: [lane0_near, lane0_far, lane1_near, lane1_far, lane2_near, lane2_far, current_lane_norm, progress_norm, speed_factor]",
+    "output_format": "3-dimensional probability array: [left_probability, stay_probability, right_probability]",
+    "action_mapping": {
+      "0": "Move Left",
+      "1": "Stay",
+      "2": "Move Right"
+    }
+  }
+}

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