tune(SNNBot): P1 cold-start with P3-target learning — 2/10 wins

Fire P1 for first COLD_K=8 exemplars (cheap misses), always store P3-correct lead offsets using P3_SPEED=11 in EVALUATE travelTime. Switches to P3 once exemplar buffer has enough data. Best observed: wins rounds 1-2 back-to-back (relative velocity encoding + P1 warmup). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-09-16 21:43:23 +02:00
parent 0384e2ba6a
commit 57cbcc5699
2 changed files with 12 additions and 5 deletions
@@ -54,3 +54,4 @@ proc learn*(aimer: var BinaryAimer, input: BitVec80, offset: float) =
  aimer.exemplars[aimer.nextSlot] = Exemplar(pattern: input, offset: offset, active: true)
  aimer.nextSlot = (aimer.nextSlot + 1) mod MAX_K
  if aimer.count < MAX_K: inc aimer.count
+