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--- |
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license: apache-2.0 |
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tags: |
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- traffic-management |
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- reinforcement-learning |
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- smart-city |
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- deep-learning |
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- pytorch |
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--- |
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# TMS2 - RL Traffic Management Models |
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## RL Traffic Signal Controller |
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Deep Q-Network (DQN) based agents for adaptive traffic signal control. |
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### Variants: |
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- **v2**: Baseline stable model optimized for throughput |
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- **v3**: Eco-friendly model with emissions optimization (14% CO2 reduction) |
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- **v4**: Challenging scenarios with incidents and demand spikes |
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- **v5/final**: Curriculum learning for generalization |
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### Architecture: |
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- Dueling Double DQN with soft target updates |
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- State space: 10-12 dimensions (queues, phase, time, scenarios) |
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- Action space: 2 (keep phase / change phase) |
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## Model Description |
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These models are part of the **Traffic Management System 2 (TMS2)** project, |
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an intelligent traffic control system using deep learning and reinforcement learning. |
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## Training Details |
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- **Framework**: PyTorch |
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- **Training Platform**: Google Colab (T4 GPU) |
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- **Training Date**: December 2025 |
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## Usage |
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```python |
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import torch |
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# Load model |
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model = torch.load('model.pt') |
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model.eval() |
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# Inference |
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with torch.no_grad(): |
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output = model(input_tensor) |
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``` |
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## License |
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Apache 2.0 |
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