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license: mit
title: VRP Solver with Transformer and OR-Tools
sdk: gradio
emoji: 🌍
colorFrom: purple
colorTo: yellow
pinned: true
short_description: Solves VRP with Transformer & RL. Compare with Google OR-Too
sdk_version: 5.33.0
datasets:
- /data
🚚 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