File size: 2,006 Bytes
5c5bfbd 08c15f4 883aafe 08c15f4 46d72f5 8f68d28 177fa02 08c15f4 9e1bb92 177fa02 08c15f4 a8b98ac 08c15f4 a8b98ac 08c15f4 16b551b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
---
license: mit
title: VRP Solver with Transformer and OR-Tools
sdk: gradio
emoji: 🌍
colorFrom: purple
colorTo: yellow
short_description: Solves VRP with Transformer & RL. Compare with Google OR-Too
sdk_version: 5.33.0
pinned: false
app_file: app.py
use_gpu: true
allow_mount: true
---
🚚 Vehicle Routing Problem Solver with Transformer-based Reinforcement Learning
This project implements a deep reinforcement learning framework to solve the Vehicle Routing Problem with Time Windows (VRPTW) using Transformer-based models. It also integrates Google OR-Tools as a classical baseline for comparison.
📁 Project Structure
bash
Copy
Edit
.
├── Actor/ # Transformer-based RL agent
├── google_solver/ # OR-Tools baseline solver
├── train_test_utils/ # Training, validation, and baseline update routines
├── utils/ # Tensor manipulation, data formatting, etc.
├── nets/ # Neural network architecture (Transformer)
├── dataloader.py # Custom dataset handling (VRP with time windows)
├── run.py # Training pipeline
├── params.json # Hyperparameters and config
└── README.md # This file
🧠 Model Description
This model is inspired by the paper
“Attention, Learn to Solve Routing Problems!”
(Bello et al., 2018 - arXiv:1803.08475)
It consists of:
A Transformer encoder that learns node embeddings
A decoding policy that uses attention for routing decisions
An actor-critic reinforcement learning strategy with a learnable baseline
Beam Search and Greedy decoding options
💾 Data Persistence
This Space writes logs, model checkpoints, and training history to the `/data` directory.
Make sure your `run.py` and `inference.py` use `/data/` for saving/loading models and results.
Example:
```python
with open("/data/train_results.txt", "a") as f:
f.write(...)
torch.save(model.state_dict(), "/data/model_state_dict.pt")
|