Update README.md

README.md

@@ -1,24 +1,70 @@
 # 🏆 VRP Shanghai Transformer
 
-> Transformer-based Reinforcement Learning Model for Vehicle Routing Problem (VRP) with Time Windows,
-> trained and tested on the Shanghai delivery dataset.
+**VRP Shanghai Transformer** is a research-grade implementation of a Transformer-based Reinforcement Learning model 
+designed to solve the **Vehicle Routing Problem (VRP)** with time windows. The model architecture 
+and learning strategy are inspired by the paper:
+
+> **"Attention, Learn to Solve Routing Problems!"**  
+> Kool, W., van Hoof, H., & Welling, M. (2019)  
+> [arXiv:1803.08475](https://arxiv.org/abs/1803.08475)
+
+This project adapts the original implementation to support the 
+**Shanghai Delivery Dataset** and includes compatibility with **Google OR-Tools** for benchmarking.
 
 ---
 
-## 🚀 Project Description
+## 📦 Features
+
+- Transformer-based Actor-Critic model for routing optimization.
+- Multi-vehicle time window routing with dynamic constraints.
+- Modular implementation with support for:
+  - Data batching and augmentation.
+  - Beam search and greedy decoding.
+  - Google OR-Tools as a baseline solver.
+- Built-in dataset loader for the Shanghai delivery tasks.
+- Easy deployment via Hugging Face Spaces (using Docker).
+
+---
 
-This project implements a deep reinforcement learning (DRL) agent based on the 
-**Transformer architecture** to solve the **Vehicle Routing Problem with Time Windows (VRPTW)**. 
-The model is adapted from the paper [Attention, Learn to Solve Routing Problems!](https://arxiv.org/abs/1803.08475) 
-and is tested on real-world delivery data from Shanghai.
+## 🔧 Tech Stack
 
-It leverages a combination of:
-- Transformer Encoder-Decoder architecture
-- Pointer Networks
-- Beam Search Decoding
-- OR-Tools for benchmarking
+- **PyTorch**: Core deep learning framework.
+- **OR-Tools**: Baseline routing solver.
+- **Docker**: Environment setup for reproducible training and inference.
+- **Hugging Face Spaces**: Deployment and interactive testing.
 
 ---
 
-## 🗂️ Project Structure
+## 🚀 Quick Start
+
+```bash
+# Clone the repository
+git clone https://github.com/your-org/vrp-shanghai-transformer.git
+cd vrp-shanghai-transformer
+
+# Run using Docker
+docker build -t vrp-transformer .
+docker run -it vrp-transformer
+
+📁 Project Structure
+
+├── Actor/                  # Model components: Actor, Graph, Fleet
+├── google_solver/          # Google OR-Tools integration
+├── train_test_utils/       # Training loop, baseline evaluation
+├── utils/                  # Helper functions (data manipulation, tensor ops)
+├── dataloader.py           # Dataset loading logic
+├── build_data.py           # Synthetic data generator
+├── README.md               # This file
+
+
+📚 References
+
+Kool, W., van Hoof, H., & Welling, M. (2019). Attention, Learn to Solve Routing Problems!
+arXiv:1803.08475
+
+Google OR-Tools: https://developers.google.com/optimization
+
+Hugging Face Spaces configuration reference:
+https://huggingface.co/docs/hub/spaces-config-reference
+