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A Decision Transformer for solving optimal EV charging problems using offline data.

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# 🚀 GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments

## ✨ Introduction
GNN-DT (Graph Neural Network Enhanced Decision Transformer) is a **next-generation AI framework** that seamlessly blends the power of **Graph Neural Networks (GNNs)** with **Decision Transformers (DTs)** to redefine decision-making in dynamic environments.

Read teh paper published in "Energy and AI":
- 📄 [Full paper](https://www.sciencedirect.com/science/article/pii/S2666546826000054)
- 📄 [Preprint](https://arxiv.org/abs/2502.01778)

🔹 **Why GNN-DT?**
- 🌟 Tackles scalability issues and sparse rewards
- 🔄 Adapts to ever-changing state-action spaces
- ⚡ Achieves unparalleled efficiency and robustness

💡 By leveraging the permutation-equivariant nature of **GNNs** and introducing an **innovative residual connection mechanism**, GNN-DT sets a new benchmark in optimization and AI-driven decision-making.

📌 This repository provides **everything you need** to explore and implement GNN-DT, including:
- ✅ Dataset generation
- ✅ Model training
- ✅ Evaluation scripts

Whether applied to **electric vehicle (EV) charging optimization** or other **complex decision-making tasks**, GNN-DT is your gateway to the **AI-driven optimization.** 🚀

![image](https://github.com/user-attachments/assets/6de6459b-e681-4f7e-ac2f-10236979b109)

---

## 🌟 Main Advantages
### 1. **Enhanced Sample Efficiency**
- 📈 Learns from previously collected trajectories, reducing the need for extensive online interactions.
- 🎯 Effectively addresses the sparse rewards limitation of traditional RL algorithms.

### 2. **Robust Generalization**
- 🌍 GNN-based embeddings allow for effective adaptation to unseen environments.
- 🔄 Handles dynamic state-action spaces with varying numbers of entities over time.

### 3. **Superior Performance**
- 🏆 Outperforms standard DT and RL baselines on real-world optimization tasks.
- 🚀 Requires significantly fewer training trajectories while achieving higher rewards.

### 4. **Scalability**
- 🔢 Maintains performance across different problem sizes without retraining.
- ⚙️ Efficiently scales from small-scale to large-scale environments, as demonstrated in EV charging applications.

![image](https://github.com/user-attachments/assets/a828ac07-564c-4ca7-889e-0a95fecd3689)

![image](https://github.com/user-attachments/assets/59c33604-e27e-43db-bfdf-2dc6c19cf914)

---

## 📖 Citation
If you find this repository useful in your research, please cite our paper:

```
@misc{orfanoudakis2025gnndt,
      title={GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments}, 
      author={Stavros Orfanoudakis and Nanda Kishor Panda and Peter Palensky and Pedro P. Vergara},
      year={2025},
      eprint={2502.01778},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2502.01778}, 
}

```

For any inquiries or contributions, feel free to open an **issue** or submit a **pull request**! 💡

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