To use a Graph Neural Network (GNN) with Reinforcement Learning (RL) to find a short path between a source and destination node, you can follow these steps:

1. Define the problem: You need to define the graph structure and the reward function. The graph structure should contain all nodes and edges, while the reward function should provide a positive reward for reaching the destination node and a negative reward for taking longer paths.

2. Train the GNN: You can train the GNN using a supervised learning approach to predict the shortest path between nodes. You can use a dataset of known shortest paths to train the GNN.

3. Implement the RL algorithm: You can use an RL algorithm such as Q-learning or SARSA to find the shortest path between the source and destination node. The RL algorithm will use the GNN to predict the shortest path and update its policy based on the rewards received.

4. Evaluate the model: You can evaluate the model by measuring the average length of the paths found by the RL algorithm and comparing it to the known shortest path length.

Overall, using a combination of GNN and RL can help find a short path between a source and destination node in a graph, even when there are multiple possible paths.