README.md

---
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
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├── 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")