Hi all,

I am working on a project where I try to implement RL with observations that are encoded as graphs. In my original RL algorithm I fill a rollout-buffer with observations (tensor shaped), which is then used for training a neural network.

I now want to move to a graph neural network. My rollout-buffer should again be filled with observations - which are now graphs with different topologies, nodes and features - to again be used for training over a minibatch. However, I am struggling with finding an efficient way to store these observations. Maybe some of you might have some ideas that could help me!

Two ideas/options I could maybe consider:

- Store the observations as tensors and somehow make a batched graph of these batched tensors before applying my GNN?
- First make the graph encodings from the original observation and storing the actual graphs in the rollout-buffer and then batching them before applying my GNN? Would this be efficient to store the actual graphs?

Any help, ideas or suggestions would be greatly appreciated!

Thanks!

Kind regards,

Erik