This research data serves as verification of our experiments and specifically includes the checkpoints during the training of our models via (multi-agent) reinforcement learning, allowing us to reconstruct the progress of the training. The weights of the trained models were serialized using the Python module pickle, and can also be reloaded using this module. In this way, the state of the model can be restored at fixed time steps (checkpoints), for example to continue the training, to evaluate the model and/or to document/archive the progress.

Diese Sammlung ist Open Access und öffentlich zugänglich.