Sounds good, out of interest, what config you are using for these experiments (e.g. the objects, the agent, how many rotations, etc.)?
Also just to clarify further, logging is currently quite limited at learning, and related to my comment about no “convergence” in supervised learning, there isn’t a meaningful measure for accuracy (or loss) as learning takes place. Since Monty can learn good representations from a single epoch of training in supervised learning, we have always just run learning and then visualized models and performed inference separately.
This would be different for unsupervised learning, where we would expect multiple epochs to be necessary to observe reasonable representations, but work on this is still in its infancy. In that case, we would want an explicit part of the training loop where we perform inference without any learning/weight updates. Alternatively, using a “prediction error” metric might enable something close to a loss during learning.