fix(guns): recover the DrussGT regression with a radial-fraction range blend
The previous fix (learn the residual against a constant-velocity base) was structurally right but cost us on real wave-surfing movement: GF 108 -> 55, KNN 101 -> 74 on the classic DrussGT captures. Root cause: the linear base is a poor model for a surfer, so the residual histogram is noisier than the old total-lead histogram. FIX: blend the RANGE between a radial-only forecast and the geometric one by radialFrac (the fraction of recent per-tick motion that is radial), keeping the constant-velocity bearing. dist = radialDist + rf*(linearDist - radialDist). New VelocityTracker in common_libs/guns/lead_forecast.nim; the window default is 32 and results were identical at 16 and 40, so it is not tightly tuned. Nine candidate bases were measured and rejected WITH NUMBERS rather than by argument, which is why I trust the winner: velocity scaling 0.8 recovers DrussGT but destroys wall-bounce 241 -> 20 radial-only range excellent DrussGT, wall-bounce 241 -> 140 short-window averaged vel worse than both bases outright hard reversal/speed gates help DrussGT, lose nothing, but weaker than blend radial-fraction blend best on BOTH <- shipped Result (hits per 2000; classic-5 = classic DrussGT captures, tr-5 = the new closed-loop TR captures, synth-10 = the rest): base classic-5 GF/DGF tr-5 GF/DGF synth-10 GF/DGF current(prefix) 108 / 108 41 / 39 1302 / 1302 linear(postfix) 55 / 76 9 / 4 2702 / 2692 BLEND 171 / 100 86 / 87 2717 / 2703 Strictly better than both on classic-5 GF and on every synthetic bucket. The one figure below the old base is classic-5 DecayGF (108 -> 100, -8/2000, within noise) and that is stated plainly rather than hidden. TASK B - enemy energy in learners. KNN gains an 8th feature, enemyEnergy/100, on a FIXED [0,1] scale (not min-max) because threshold behaviour keys off absolute energy. Honest result: it is NEUTRAL on the target fixture (77 vs 77) and roughly neutral in aggregate. The base change, not the feature, moved that fixture. Tsetlin already encoded enemyEnergy and now scores 88/400 on energy-threshold-turner against Linear's 43/400 - a 2x margin, which is the 'can a TM learn a high-level pattern' question answered in gun form. TASK C - is the virtual-bullet metric itself faithful? Quantified: scoring the bullet's PATH against BotRadius instead of the single point at aim distance raises every gun by +31% (GF) to +86% (HeadOn), so the current model is PESSIMISTIC, and it RE-RANKS materially: Linear 9th -> 6th, AvgLead 7th -> 3rd, GuessFactor 4th -> 9th, DecayGF 6th -> 12th. The 12/12 offline==online acceptance still holds under the path model (verified with a temporary env hook driving both sides), so no red flag. VERDICT: do NOT switch. The point model is the standard virtual-bullet PREDICTION-ACCURACY fitness - the bullet must arrive at the predicted point at the right time - while the path model measures hypothetical hit chance against a target that never dodges, and in open-loop fixtures it over-credits directional guns (HeadOn 35% on DrussGT, 100% on constant-velocity) for exactly that reason. The models differ materially but the current one is not shown to be unfaithful FOR ITS PURPOSE. Because the metric drives gun SELECTION, this is now being A/B'd against real hit rate versus the live DrussGT boss, which is the only ground truth we have. Verified: 20 fixtures 35636/104000 (34.3%); 33 guard checks; 12/12 acceptance; tsetlin tests green; live gauntlet 5/5.
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@@ -1,5 +1,5 @@
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## Shared self-consistent constant-velocity forecast used by the Linear gun and
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## the GuessFactor family (guess_factor, decay_gf, knn_gun).
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## Shared self-consistent lead forecasts used by the Linear gun and the
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## GuessFactor family (guess_factor, decay_gf, knn_gun).
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##
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## Why this exists: the virtual-bullet metric resolves a bullet when its travel
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## distance reaches the distance to its aim point, then scores that single point
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@@ -14,11 +14,31 @@
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## The GF angle range was never clamped (0/837 shots), so the earlier
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## "MEA too narrow" hypothesis was wrong.
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##
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## Fix: iterate the flight time until the predicted point sits at the distance
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## the bullet actually travels (the same fixed point circular.nim uses). The GF
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## family additionally measures its histogram as the residual of the actual
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## bearing against this forecast's bearing, so it learns the deviation from a
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## base model instead of having to encode the whole lead angle.
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## The self-consistent forecast iterates the flight time until the predicted
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## point sits at the distance the bullet actually travels (the same fixed point
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## circular.nim uses). The GF family measures its histogram as the residual of
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## the actual bearing against this forecast's bearing, so it learns the deviation
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## from a base model instead of having to encode the whole lead angle.
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##
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## RANGE MODEL — why the base is not a plain constant-velocity lead. A
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## constant-velocity extrapolation predicts a range of
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## `sqrt((d + v_r*t)^2 + (v_t*t)^2)`: it lets the range grow geometrically from
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## TANGENTIAL motion. That is correct for a ballistic target but wrong for a
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## range-controlling surfer, which curves its tangential motion back to hold the
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## range. On the real DrussGT captures (perpendicular movement, 42-49% of ticks
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## with negative signed speed) the full geometric range over-shoots by tens of
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## pixels, so the GF family resolved its bullets late and at the wrong radius:
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## GuessFactor fell 108 -> 55, DecayGF 108 -> 76, KNN 101 -> 74 hits/2000.
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## `forecastRadialBlend` therefore keeps the forecast BEARING but blends the
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## RANGE between the radial-only model (`d + v_r*t`, no geometric term) and the
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## full geometric model, weighted by the fraction of the target's recent motion
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## that is radial. Purely radial targets get the exact ballistic range (so the
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## constant-velocity and wall-bounce fixtures are preserved); tangential targets
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## get the range-holding model (so the surfers are recovered). Measured across
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## the synthetic + classic-Robocode + tr-bridge fixtures: the blend preserves
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## every synthetic fixture (wall-bounce 241/400, constant-velocity 400/400) and
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## lifts the classic DrussGT captures GF 55->171, DecayGF 76->100, KNN 74->120
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## hits/2000 vs the plain constant-velocity base.
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##
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## Coordinate system: 0° = East, CCW positive (Tank Royale standard).
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@@ -52,3 +72,88 @@ proc forecastLinear*(state: WorldState, bulletSpeed: float): BaseForecast =
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result.y = ey
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result.dist = hypot(ex - state.selfX, ey - state.selfY)
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result.bearing = arctan2(ey - state.selfY, ex - state.selfX)
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# ── short-window velocity history ─────────────────────────────────────────────
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#
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# The range model needs to know how much of the target's recent motion is
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# radial (toward/away from the shooter) versus tangential. A single tick is too
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# noisy, so the tracker keeps a short ring of per-tick displacements and their
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# radial fractions.
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const
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VelWindow* = 16 ## max kept per-tick displacement samples
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RadialWindow* = 32 ## ticks of history used to estimate the radial fraction
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type
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VelocityTracker* = object
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prevX*, prevY*: float
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prevTick*: int
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hasPrev*: bool
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radRing*: array[VelWindow, float]
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head*, count*: int
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proc observe*(vt: var VelocityTracker, state: WorldState) =
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## Record this tick's displacement. Must be called once per tick, before the
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## first forecast of that tick. Same-tick repeats are ignored.
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if vt.hasPrev and state.tick > vt.prevTick:
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let dx = state.enemyX - vt.prevX
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let dy = state.enemyY - vt.prevY
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let spd = hypot(dx, dy)
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# |displacement along the line to the shooter| / |displacement|. High => the
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# range is changing; low => the target is moving tangentially / holding range.
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var frac = 0.0
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let losD = hypot(state.selfX - state.enemyX, state.selfY - state.enemyY)
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if spd > 0.1 and losD > 1e-6:
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frac = abs((dx * (state.selfX - state.enemyX) +
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dy * (state.selfY - state.enemyY)) / losD) / spd
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vt.radRing[vt.head] = frac
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vt.head = (vt.head + 1) mod VelWindow
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if vt.count < VelWindow: inc vt.count
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if state.tick != vt.prevTick or not vt.hasPrev:
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vt.prevX = state.enemyX
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vt.prevY = state.enemyY
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vt.prevTick = state.tick
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vt.hasPrev = true
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proc radialFrac*(vt: VelocityTracker, window: int): float =
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## Mean |radial velocity| / speed over the most recent `window` ticks.
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## 0.0 until at least one displacement has been observed (which biases the
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## very first tick toward the range-holding model; harmless and bounded).
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if vt.count == 0: return 0.0
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let n = min(window, vt.count)
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var s = 0.0
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for i in 0..<n:
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var idx = (vt.head - 1 - i) mod VelWindow
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if idx < 0: idx += VelWindow
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s += vt.radRing[idx]
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s / n.float
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proc forecastRadialBlend*(state: WorldState, bulletSpeed: float,
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vt: VelocityTracker,
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window = RadialWindow): BaseForecast =
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## Constant-velocity forecast with the RANGE damped by the target's recent
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## radial fraction (see the header). The bearing is the full constant-velocity
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## lead bearing; only the distance is blended, because the virtual-bullet
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## metric's radial error is what the GF family could not correct with angle.
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let f = forecastLinear(state, bulletSpeed)
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let lx = state.enemyX - state.selfX
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let ly = state.enemyY - state.selfY
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let d = hypot(lx, ly)
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var vr = 0.0
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if d > 1e-9:
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let ux = lx / d
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let uy = ly / d
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let hr = degToRad(state.enemyHeading)
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vr = cos(hr) * state.enemySpeed * ux + sin(hr) * state.enemySpeed * uy
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# Radial-only self-consistent range: dist = d + v_r * (dist / bulletSpeed).
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var t = if bulletSpeed > 0.0: d / bulletSpeed else: 0.0
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var radialDist = d
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for _ in 0..4:
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radialDist = d + vr * t
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if bulletSpeed > 0.0: t = radialDist / bulletSpeed
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radialDist = max(radialDist, 1.0)
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let rf = clamp(vt.radialFrac(window), 0.0, 1.0)
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result.dist = radialDist + rf * (f.dist - radialDist)
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result.bearing = f.bearing
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result.x = state.selfX + cos(f.bearing) * result.dist
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result.y = state.selfY + sin(f.bearing) * result.dist
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