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Kunalsinghh
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tms-rl-traffic-controller

Reinforcement Learning
PyTorch
traffic-management
smart-city
deep-learning
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Upload rl models

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  1. .gitattributes +3 -0
  2. README.md +55 -0
  3. adaptive_dueling_ep100.pt +3 -0
  4. adaptive_dueling_ep1000.pt +3 -0
  5. adaptive_dueling_ep1100.pt +3 -0
  6. adaptive_dueling_ep1200.pt +3 -0
  7. adaptive_dueling_ep1300.pt +3 -0
  8. adaptive_dueling_ep1400.pt +3 -0
  9. adaptive_dueling_ep1500.pt +3 -0
  10. adaptive_dueling_ep200.pt +3 -0
  11. adaptive_dueling_ep300.pt +3 -0
  12. adaptive_dueling_ep400.pt +3 -0
  13. adaptive_dueling_ep500.pt +3 -0
  14. adaptive_dueling_ep600.pt +3 -0
  15. adaptive_dueling_ep700.pt +3 -0
  16. adaptive_dueling_ep800.pt +3 -0
  17. adaptive_dueling_ep900.pt +3 -0
  18. adaptive_dueling_final.pt +3 -0
  19. best_model.pt +3 -0
  20. dataset-metadata.json +16 -0
  21. double_dqn_best.pt +3 -0
  22. double_dqn_ep100.pt +3 -0
  23. double_dqn_ep1000.pt +3 -0
  24. double_dqn_ep200.pt +3 -0
  25. double_dqn_ep300.pt +3 -0
  26. double_dqn_ep400.pt +3 -0
  27. double_dqn_ep500.pt +3 -0
  28. double_dqn_ep600.pt +3 -0
  29. double_dqn_ep700.pt +3 -0
  30. double_dqn_ep800.pt +3 -0
  31. double_dqn_ep900.pt +3 -0
  32. double_dqn_final.pt +3 -0
  33. dqn_best.pt +3 -0
  34. dqn_ep100.pt +3 -0
  35. dqn_ep1000.pt +3 -0
  36. dqn_ep200.pt +3 -0
  37. dqn_ep300.pt +3 -0
  38. dqn_ep400.pt +3 -0
  39. dqn_ep500.pt +3 -0
  40. dqn_ep600.pt +3 -0
  41. dqn_ep700.pt +3 -0
  42. dqn_ep800.pt +3 -0
  43. dqn_ep900.pt +3 -0
  44. dqn_final.pt +3 -0
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  46. dueling_dqn_ep100.pt +3 -0
  47. dueling_dqn_ep1000.pt +3 -0
  48. dueling_dqn_ep200.pt +3 -0
  49. dueling_dqn_ep300.pt +3 -0
  50. dueling_dqn_ep400.pt +3 -0
.gitattributes CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ rl/training_comparison.png filter=lfs diff=lfs merge=lfs -text
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+ rl_v2/training_comparison_v2.png filter=lfs diff=lfs merge=lfs -text
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+ rl_v3/training_comparison_v3_emissions.png filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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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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+
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+ # TMS2 - RL Traffic Management Models
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+
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+ ## RL Traffic Signal Controller
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+
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+ Deep Q-Network (DQN) based agents for adaptive traffic signal control.
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+
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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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+
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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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+
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+ ## Model Description
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+
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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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+
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+ ## Training Details
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+
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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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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+
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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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+
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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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+
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+ ## License
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+
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+ Apache 2.0
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+ {
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+ "title": "TMS RL Traffic Signal Controllers",
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+ "id": "Kunalsinghh25/tms2-rl-models",
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+ "name": "Apache 2.0"
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+ "traffic",
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