Store in the hyper_df generated from the search multiple objective/target values that summarize performance. Currently only single one is stored. In update print message simply print configuration for each one of the metrics.
Store in the hyper_df generated from the search a path pointer for the final saved checkpoint. This makes loading the trained agent back in smoother and allows for faster post-processing afterwards. If there are multiple random seeds only store one of them.
Store in the hyper_df generated from the search all different raw random seed ids on which we trained and the number of seeds that were aggregated for the scores.
Store in the hyper_df generated from the search multiple objective/target values that summarize performance. Currently only single one is stored. In update print message simply print configuration for each one of the metrics.
Store in the hyper_df generated from the search a path pointer for the final saved checkpoint. This makes loading the trained agent back in smoother and allows for faster post-processing afterwards. If there are multiple random seeds only store one of them.
Store in the hyper_df generated from the search all different raw random seed ids on which we trained and the number of seeds that were aggregated for the scores.