The OPARA service was recently upgraded to a new technical platform. You are visiting the outdated OPARA website. Please use for new data submissions. Previously stored data will be migrated in near future and then the old version of OPARA will finally be shut down. Existing DOIs for data publications remain valid.

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.

This collection is open access and publicly accessible.