DC and VCS
DrussGT uses a "bullets shot" classifier to give higher weight to newer data and works somewhat like data decay. It makes k-NN search extrapolate a bit, but still helps increase the score against learning opponents.
IMHO, the advantage of VCS over DC is CPU performance. But VCS compresses data into bins and some information is lost, while in DC it isn´t. So, well tuned DC should perform better than well tuned VCS, unless you start skipping turns.
That is for the gun, but a similar thing is definitely useful in movement. Best would be one log which has data rolling, and one without to handle simple bots and make sure you don't forget anything about them. That trick actually comes from Rednaxela, and possibly even originally ABC. It causes the Kd-Tree to be a bit slower as the match progresses, but works wonders against surfers and is behind my recent-ish PL improvements.
I have a similar view on VCS vs DC - VCS is faster at lookups (obviously, index lookups are O(1)) but less accurate due to the discretisation of both the bins and the segments. However, even in a DC environment VCS has its place: if I were surfing DC I would cache my results in an array just like VCS to make lookups faster when evaluating surfing points. Note, DrussGT's movement doesn't actually use segmented VCS in the movement anymore, but instead lists of hit indexes in each segment. This reduced my storage space and my logging-hits time, and actually reduced my retrieve time as well because many of the hits are different representations of the same original hit (from my many buffers). I did some trickery with weighting the hits to make sure it gives exactly the same results as my VCS with a rolling average would have.
It's worth noting that in movement, you have far less data, so CPU speed is less of an issue. Until you get into flatteners, and even then maybe virtual wave flatteners (which are rare), DC surfing could probably do fine without even using kd-trees. Precise prediction far outweighs information management, I think.
And yes, having a dimension that is pure linear time is certainly one of the simpler approaches to KNN data decay... ;)
What I was actually doing in my gun was having a non-linear time dimension, and it worked out a lot better than straight linear. I think I ended up with 1.2*T^(0.4) or so, instead of 0.005*T. Those weights were genetically evolved with my WaveSim-ish setup.
It makes sense that at the beginning you want your data to decay faster than towards the end...
I've generally considered the time dimension a pretty blunt/naive approach (I think I used it in Lukious), but I've been mulling it for the last hour and now I'm thinking it's actually pretty reasonable. I might even tinker with it some in Diamond (especially now that I have your secret formula, muahahaha!).
I know I've posted it elsewhere, but the main system I use in decaying Diamond's surf stats is like this:
- No time dimension, but each data point is timestamped.
- After choosing k neighbors, sort them inverse chronologically and weight them by rank. (Eg, 16/8/4/2/1.)
- Optionally (like in flattener), I also cap the size of the log and remove the oldest points.
Tuning k is like adjusting the granularity of a VCS buffer: k=1 is like a super highly segmented buffer (most similar situation wins, regardless of age), while a larger k is like a less segmented buffer with rolling average.
While I've tried this in my Anti-Surfer gun, too, the most effective decay I've found there is just capping the size of the log and discarding old points.
