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You can view and copy the source of this page.Rednaxela (talk)
Return to Thread:Talk:BeepBoop/Awesome enty/reply (16).
Including several ticks of history seems like a nice way of removing the need for hand-crafted features like acceleration, time-since-velocity-change, distance-last-k-ticks, etc., and having the model learn them instead. Maybe a good model could even learn some PM-like behaviors.
Definitely a weakness of KNNs is generalization to new parts of the input space. I did think a bit about pre-training a model against a lot of bots and then quickly adapting it to the current opponent (maybe using meta-learning methods) so it would generalize better early in the match before it gets lots of data. On the other hand, aiming models get a lot of data pretty quickly, so I'm not sure of how much of an issue poor generalization really is.
I would say it probably depends what you're targeting. When targeting a strong surfer, I would say there's potentially a lot of value in maximizing the utility of data learned since the surfer last got information from collisions, and so that's a scenario where generalizing seems potentially more important in my eyes.
(unless it's going 100% flattener, in which case I would say the value is adapting on time scales that are simply different from what it's flattening over, either learning faster the flattener, or learning long-term "history repeating itself" trends/patterns that it loses sight of)
Also some deep enough model can learn how surfers (without flattening) "surf" hits, just like networks like "Deep Interest Network" used in CTR that learns how users' interest change over time. However our current use of KNN allows nothing like this. Maybe some end-to-end approach exists for robocode scenario.
My past experiments (shallow NN that does online-learning without pre-training) with end-to-end approach didn't yield anything interesting though.