---
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}
}
```