Considerations for Seeding¶
Seeding is important both on the level of RL algorithms as well as the AutoRL level. In general, we propose to use three different set of random seeds for training, validation, and testing.
For training and validation, ARLBench takes care of the seeding. When you pass a seed to the AutoRL Environment, it uses this seed for training but seed + 1 for the validation during training. We recommend to use seeds 0 - 9 for training and validation, i.e., by passing them to the AutoRL Environment for the tuning process. You are of course free to increase this range, but we recommend to use at least 10 different seeds for reliable results.
When it comes to testing HPO methods, we provide a evaluation script in our examples. We propose to use seeds 100, 101, … here to make sure the method is tested on a different set of random seeds. Here we suggest three HPO runs as a minimum even for stable optimizers - for consistent results with small confidence intervals, you should like aim for more runs.