feat(SNNBot): offset-based exemplar learning for fast lead generalization
Exemplars now store lead correction offsets instead of absolute angles. The system learns 'how much lead to apply' independently of bearing, so one learned lead pattern generalizes to all positions on the battlefield. Converges in a few ticks for constant-velocity targets.
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@@ -215,6 +215,7 @@ type
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lastDecideGunDir: float # gun heading captured at DECIDE time for EVALUATE
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lastBinInput: BitVec80 # DECIDE-time binary input, reused in EVALUATE
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lastInput: BitVec80 # previous EVALUATE-time input (for change detection)
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lastAbsBearing: float # absolute bearing to enemy captured at DECIDE time
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# ── aimTo helper ──────────────────────────────────────────────────────────────
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@@ -340,17 +341,18 @@ method run*(bot: SNNBot) =
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bot.lastRelBearing = normalizeRelativeAngle(absBearing - gunDir)
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when USE_RESERVOIR:
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let binInput = toBinaryInput(absBearing, bot.velDirDeg, bot.velSpeed, bot.hasLastPos)
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let aimRaw = bot.res.forward(binInput) # [0,360) or -1.0 sentinel
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let aimOffset = bot.res.forward(binInput) # lead correction offset or -999.0 sentinel
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bot.lastBinInput = binInput
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bot.lastDecideGunDir = gunDir
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if aimRaw < 0.0:
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# cold start: aim directly at enemy
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bot.lastAbsBearing = absBearing
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if aimOffset <= -999.0:
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# cold start: aim directly at enemy (no lead)
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bot.targetAngle = absBearing
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echo "RES tick=" & $bot.tick & " cold-start aim=" & formatFloat(absBearing, ffDecimal, 1)
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else:
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# aimRaw is absolute [0,360): use directly as target
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bot.targetAngle = aimRaw
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echo "RES tick=" & $bot.tick & " aim=" & formatFloat(aimRaw, ffDecimal, 1)
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# apply lead correction offset to current bearing
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bot.targetAngle = (absBearing + aimOffset + 360.0) mod 360.0
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echo "RES tick=" & $bot.tick & " offset=" & formatFloat(aimOffset, ffDecimal, 1) & " aim=" & formatFloat(bot.targetAngle, ffDecimal, 1)
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else:
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let inputs = encodeInputFull(absBearing, bot.velDirDeg, bot.velSpeed, bot.hasLastPos)
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# Multi-tick inference: accumulate sin/cos and spike counts over N_INFER ticks
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@@ -410,24 +412,26 @@ method run*(bot: SNNBot) =
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let adaptiveDeadZone = 0.5 + (0.3 - 0.5) * t # lerp(0.5, 0.3, t)
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when USE_RESERVOIR:
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let aimRaw = bot.res.forward(bot.lastBinInput)
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# correctOffset: how much lead to apply from current bearing
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let correctOffset = normalizeRelativeAngle(correctAngle - bot.lastAbsBearing)
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let producedOffset = bot.res.forward(bot.lastBinInput)
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# Detect significant input change via Hamming distance
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let hammingDist = popcount(bot.lastBinInput[0] xor bot.lastInput[0]) + popcount(bot.lastBinInput[1] xor bot.lastInput[1])
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let inputChanged = hammingDist > 2 # 3+ bits flipped = situation changed
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# Learn if: error > dead zone OR input pattern changed significantly
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if aimRaw >= 0.0:
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let aimErr = abs(normalizeRelativeAngle(aimRaw - correctAngle))
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if producedOffset > -999.0:
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let aimErr = abs(normalizeRelativeAngle(producedOffset - correctOffset))
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if aimErr > adaptiveDeadZone or inputChanged:
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bot.res.learn(bot.lastBinInput, correctAngle)
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bot.res.learn(bot.lastBinInput, correctOffset)
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else:
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# Cold start: always learn
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bot.res.learn(bot.lastBinInput, correctAngle)
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let aimErr = if aimRaw >= 0.0: abs(normalizeRelativeAngle(aimRaw - correctAngle)) else: -1.0
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bot.res.learn(bot.lastBinInput, correctOffset)
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let aimErr = if producedOffset > -999.0: abs(normalizeRelativeAngle(producedOffset - correctOffset)) else: -1.0
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echo "RES tick=" & $bot.tick &
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" aim=" & (if aimRaw >= 0.0: formatFloat(aimRaw, ffDecimal, 1) else: "cold") &
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" correct=" & formatFloat(correctAngle, ffDecimal, 1) &
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" err=" & (if aimRaw >= 0.0: formatFloat(aimErr, ffDecimal, 1) else: "n/a") &
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" offset=" & (if producedOffset > -999.0: formatFloat(producedOffset, ffDecimal, 1) else: "cold") &
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" correctOffset=" & formatFloat(correctOffset, ffDecimal, 1) &
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" err=" & (if producedOffset > -999.0: formatFloat(aimErr, ffDecimal, 1) else: "n/a") &
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" hammingDist=" & $hammingDist &
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" inputChanged=" & $inputChanged &
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" exemplars=" & $bot.res.count
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