What's the highest ranking non-learning bot in the rumble?

I think about learning more like, results in the past can influence future decisions. f.e. Linear targeting only knows about current state (radarinfo), while Circular targeting knows about current state and previous state. Both do not learn, as similar states give similar decisions, without any correlation to past results.

Well, we can loosen the limit of perceptual to allow information of k recent turns, e.g. k-perceptual. Under this definition, linear targeting will be 1-perceptual, circular targeting being 2-perceptual, and average velocity targeting with window size k will be k-perceptual.

As long as k is large enough, we can still make effective learning methods. So there’s still not an absolute difference between learning and non-learning. Simple enough methods like averaging velocity is still “learning”.

This is pretty much what I had in mind, particularly "similar states give similar decisions". Another distinction I find helpful is the idea that past results should not influence decisions in the future but past states can. A gun that gathers statistics on hit/miss and adjusts its aim is learning but average velocity targeting is not.

If average velocity (with window size k) is non-learning, how about play it forward using only k recent scans? Both are not changing behavior based on results, only recent scans, plus, play it forward is merely a more precise version of “averageing”.

IMO both are learning using k scans, the only difference is the latter is more precise, and using data more effectively.

I definitely agree that they're both learning, at least in some sense. The main difference is that the accuracy of a linear targeting system would have diminishing returns as k is increased, whereas a PIF system would likely benefit from increased amounts of data.

It's certainly a difficult question to formalise. I suppose that plotting prediction accuracy vs. k would give some idea of how much learning is going on.