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feat(SNNBot): offset-based exemplar learning for fast lead generalization

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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.
This commit is contained in:
Davide Cappellini
2026-09-16 08:12:54 +02:00
parent 01a30b7570
commit 69ffe4c44e
2 changed files with 26 additions and 24 deletions
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SNNBot_garage/src/SNNBot.nim
+19 -15
View File
@@ -215,6 +215,7 @@ type
lastDecideGunDir: float # gun heading captured at DECIDE time for EVALUATE
lastBinInput: BitVec80 # DECIDE-time binary input, reused in EVALUATE
lastInput: BitVec80 # previous EVALUATE-time input (for change detection)
lastAbsBearing: float # absolute bearing to enemy captured at DECIDE time
# ── aimTo helper ──────────────────────────────────────────────────────────────
@@ -340,17 +341,18 @@ method run*(bot: SNNBot) =
bot.lastRelBearing = normalizeRelativeAngle(absBearing - gunDir)
when USE_RESERVOIR:
let binInput = toBinaryInput(absBearing, bot.velDirDeg, bot.velSpeed, bot.hasLastPos)
let aimRaw = bot.res.forward(binInput) # [0,360) or -1.0 sentinel
let aimOffset = bot.res.forward(binInput) # lead correction offset or -999.0 sentinel
bot.lastBinInput = binInput
bot.lastDecideGunDir = gunDir
if aimRaw < 0.0:
# cold start: aim directly at enemy
bot.lastAbsBearing = absBearing
if aimOffset <= -999.0:
# cold start: aim directly at enemy (no lead)
bot.targetAngle = absBearing
echo "RES tick=" & $bot.tick & " cold-start aim=" & formatFloat(absBearing, ffDecimal, 1)
else:
# aimRaw is absolute [0,360): use directly as target
bot.targetAngle = aimRaw
echo "RES tick=" & $bot.tick & " aim=" & formatFloat(aimRaw, ffDecimal, 1)
# apply lead correction offset to current bearing
bot.targetAngle = (absBearing + aimOffset + 360.0) mod 360.0
echo "RES tick=" & $bot.tick & " offset=" & formatFloat(aimOffset, ffDecimal, 1) & " aim=" & formatFloat(bot.targetAngle, ffDecimal, 1)
else:
let inputs = encodeInputFull(absBearing, bot.velDirDeg, bot.velSpeed, bot.hasLastPos)
# Multi-tick inference: accumulate sin/cos and spike counts over N_INFER ticks
@@ -410,24 +412,26 @@ method run*(bot: SNNBot) =
let adaptiveDeadZone = 0.5 + (0.3 - 0.5) * t # lerp(0.5, 0.3, t)
when USE_RESERVOIR:
let aimRaw = bot.res.forward(bot.lastBinInput)
# correctOffset: how much lead to apply from current bearing
let correctOffset = normalizeRelativeAngle(correctAngle - bot.lastAbsBearing)
let producedOffset = bot.res.forward(bot.lastBinInput)
# Detect significant input change via Hamming distance
let hammingDist = popcount(bot.lastBinInput[0] xor bot.lastInput[0]) + popcount(bot.lastBinInput[1] xor bot.lastInput[1])
let inputChanged = hammingDist > 2 # 3+ bits flipped = situation changed
# Learn if: error > dead zone OR input pattern changed significantly
if aimRaw >= 0.0:
let aimErr = abs(normalizeRelativeAngle(aimRaw - correctAngle))
if producedOffset > -999.0:
let aimErr = abs(normalizeRelativeAngle(producedOffset - correctOffset))
if aimErr > adaptiveDeadZone or inputChanged:
bot.res.learn(bot.lastBinInput, correctAngle)
bot.res.learn(bot.lastBinInput, correctOffset)
else:
# Cold start: always learn
bot.res.learn(bot.lastBinInput, correctAngle)
let aimErr = if aimRaw >= 0.0: abs(normalizeRelativeAngle(aimRaw - correctAngle)) else: -1.0
bot.res.learn(bot.lastBinInput, correctOffset)
let aimErr = if producedOffset > -999.0: abs(normalizeRelativeAngle(producedOffset - correctOffset)) else: -1.0
echo "RES tick=" & $bot.tick &
" aim=" & (if aimRaw >= 0.0: formatFloat(aimRaw, ffDecimal, 1) else: "cold") &
" correct=" & formatFloat(correctAngle, ffDecimal, 1) &
" err=" & (if aimRaw >= 0.0: formatFloat(aimErr, ffDecimal, 1) else: "n/a") &
" offset=" & (if producedOffset > -999.0: formatFloat(producedOffset, ffDecimal, 1) else: "cold") &
" correctOffset=" & formatFloat(correctOffset, ffDecimal, 1) &
" err=" & (if producedOffset > -999.0: formatFloat(aimErr, ffDecimal, 1) else: "n/a") &
" hammingDist=" & $hammingDist &
" inputChanged=" & $inputChanged &
" exemplars=" & $bot.res.count
SNNBot_garage/src/reservoir.nim
+7 -9
View File
@@ -13,7 +13,7 @@ type
Exemplar* = object
pattern*: BitVec80
angle*: float # degrees 0-360
offset*: float # lead correction in degrees, typically [-30, +30]
active*: bool
BinaryAimer* = object
@@ -27,7 +27,7 @@ proc initBinaryAimer*(): BinaryAimer =
proc forward*(aimer: var BinaryAimer, input: BitVec80): float =
## Kernel regression: weighted circular mean over active exemplars.
## Weights by similarity^2 and exponential recency decay (0.95^age).
## Returns angle in [0,360) degrees, or -1.0 sentinel when cold (no weight).
## Returns offset (lead correction) in degrees, or -999.0 sentinel when cold (no weight).
var sinSum = 0.0
var cosSum = 0.0
var totalW = 0.0
@@ -41,18 +41,16 @@ proc forward*(aimer: var BinaryAimer, input: BitVec80): float =
let age = (aimer.nextSlot - 1 - i + MAX_K) mod MAX_K
let recency = pow(0.95, age.float)
let weight = float(excess * excess) * recency
let rad = degToRad(aimer.exemplars[i].angle)
let rad = degToRad(aimer.exemplars[i].offset)
sinSum += weight * sin(rad)
cosSum += weight * cos(rad)
totalW += weight
if totalW == 0.0:
return -1.0
var angle = radToDeg(arctan2(sinSum, cosSum))
if angle < 0.0: angle += 360.0
result = angle
return -999.0
result = radToDeg(arctan2(sinSum, cosSum))
proc learn*(aimer: var BinaryAimer, input: BitVec80, correctAngle: float) =
proc learn*(aimer: var BinaryAimer, input: BitVec80, offset: float) =
## Ring-buffer store: write exemplar at nextSlot, advance mod MAX_K.
aimer.exemplars[aimer.nextSlot] = Exemplar(pattern: input, angle: correctAngle, active: true)
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