Machine Learning, AI, Reinforcement Learning, Optimization, Neural Networks.
the stability of our model during training. In our case, efficiency is much more important than stability, because we’re limited to a fixed number of experiments. And unlike most applications, we won’t be re-using the trained model for anything — we just use it to estimate hyperparameters, and then