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

TL;DR

GNN-DT is a novel architecture combining Graph Neural Networks with Decision Transformers to improve optimization efficiency, scalability, and generalization in dynamic, sparse reward environments like EV charging.

Contribution

It introduces GNN-DT, integrating GNNs with Decision Transformers and residual connections, enhancing sample efficiency and robustness in dynamic RL tasks.

Findings

Outperforms existing DT and RL baselines in EV charging optimization

Requires fewer training trajectories for high-quality solutions

Shows strong generalization to unseen environments and larger action spaces

Abstract

Reinforcement Learning (RL) methods used for solving real-world optimization problems often involve dynamic state-action spaces, larger scale, and sparse rewards, leading to significant challenges in convergence, scalability, and efficient exploration of the solution space. This study introduces GNN-DT, a novel Decision Transformer (DT) architecture that integrates Graph Neural Network (GNN) embedders with a novel residual connection between input and output tokens crucial for handling dynamic environments. By learning from previously collected trajectories, GNN-DT tackles the sparse rewards limitations of online RL algorithms and delivers high-quality solutions in real-time. We evaluate GNN-DT on the complex electric vehicle (EV) charging optimization problem and prove that its performance is superior and requires significantly fewer training trajectories, thus improving sample efficiency compared to existing DT and offline RL baselines. Furthermore, GNN-DT exhibits robust generalization to unseen environments and larger action spaces, addressing a critical gap in prior offline and online RL approaches.