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