Thread:Talk:DrussGT/Version History/3.1.3DC vs 3.1.3/reply (18)

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# There are some high level movement classes that are worth segmenting. Against simple targeters, time since velocity change is just noise. Against most bots, a flattener would be noise. But for a bot where a flattener helps, those lower levels of stats don't hurt. I think they even add "harmless noise" - they are still bullet dodging, so they won't make horrible decisions. So I have a few tiers (simple, normal / decaying, light flattener, flattener) in my movement stats, enabled at different enemy hit percentages.
 
# There are some high level movement classes that are worth segmenting. Against simple targeters, time since velocity change is just noise. Against most bots, a flattener would be noise. But for a bot where a flattener helps, those lower levels of stats don't hurt. I think they even add "harmless noise" - they are still bullet dodging, so they won't make horrible decisions. So I have a few tiers (simple, normal / decaying, light flattener, flattener) in my movement stats, enabled at different enemy hit percentages.
# I found VCS to be easier to tune that DC. Similarly, I think layering a few trees is easier than trying to add features to your KNN system to create the exact "shapes" (or however you imagine it) that you want. "5 of last 150 + 5 of last 500 + 5 of last 1500" is easy to understand. Adjusting the weights and distancing to produce the same results from one KNN call seems hard.
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# I found VCS to be easier to tune than DC. Similarly, I think layering a few trees is easier than trying to add features to your KNN system to create the exact "shapes" (or however you imagine it) that you want. "5 of last 150 + 5 of last 500 + 5 of last 1500" is easy to understand. Adjusting the weights and distancing to produce the same results from one KNN call seems hard.
 
# I can't prove that it is.
 
# I can't prove that it is.

Latest revision as of 15:51, 16 January 2014

I'd answer this in 3 parts.

  1. There are some high level movement classes that are worth segmenting. Against simple targeters, time since velocity change is just noise. Against most bots, a flattener would be noise. But for a bot where a flattener helps, those lower levels of stats don't hurt. I think they even add "harmless noise" - they are still bullet dodging, so they won't make horrible decisions. So I have a few tiers (simple, normal / decaying, light flattener, flattener) in my movement stats, enabled at different enemy hit percentages.
  2. I found VCS to be easier to tune than DC. Similarly, I think layering a few trees is easier than trying to add features to your KNN system to create the exact "shapes" (or however you imagine it) that you want. "5 of last 150 + 5 of last 500 + 5 of last 1500" is easy to understand. Adjusting the weights and distancing to produce the same results from one KNN call seems hard.
  3. I can't prove that it is.
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