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.
The entire GuessFactor family scored 0% on clean circular and wall-bounce
trajectories. Two hypotheses were on the table and BOTH were wrong:
- MEA range too narrow / edge clamping: REFUTED. Measured 0 clamped shots
out of 837/849/957, required offsets peak at ~33 deg against MEA
28.1-46.7 deg, and the 8 in arcsin(8/bulletSpeed) is correct (it is the max
robot SPEED, not the hit radius). Changing it to BotRadius=18 would have
coarsened resolution for nothing.
- Peak selection: REFUTED. A sweep of every constant GF value showed the
ORACLE-BEST constant offset on the original gun was only 6% circular,
4% wall-bounce, 7.5% random-walk. No peak choice could have done better.
The learning path was fine too: ~850-960 observations per fixture, 0
starved waves, well-populated histograms.
REAL CAUSE: the GF family aimed at the FIRE-TIME distance. The virtual-bullet
metric resolves a bullet at the AIM-POINT distance and scores that single
point against the enemy's position on that tick, so with any radial target
motion the bullet stops at the wrong radius and misses even with a perfect
angle. Angle-only prediction is structurally unscoreable under this metric.
FIX: give the GF family a self-consistent constant-velocity forecast as its
base reference (new common_libs/guns/lead_forecast.nim, which iterates the
flight time to the same fixed point circular.nim uses), so the histogram
learns the RESIDUAL against that forecast and the aim point lands at the
right radius. Applied to guess_factor, decay_gf and knn_gun.
Same defect fixed in Linear: it did a one-shot dist/bulletSpeed extrapolation
and never iterated its flight time.
The oracle sweep proves the structural fix, independently of tuning: the best
achievable constant GF moved 6% -> 20% (circular), 4% -> 57% (wall-bounce),
7.5% -> 49% (random-walk).
MEASURED, all 15 fixtures: total 39.0% -> 44.4% (30399 -> 34654 hits).
circular GF 6 -> 23, DecayGF 6 -> 21
wall-bounce GF 0 -> 60.2, DecayGF 0 -> 60.2
constant-vel GF 26 -> 100, DecayGF 26 -> 100, KNN 26 -> 100, Linear 87 -> 100
random-walk GF 0 -> 53, DecayGF 0 -> 52, Linear 24 -> 53
StraightLine GF 8 -> 77, DecayGF 8 -> 77
Non-regression: 33 guard checks pass, the range's 12/12 offline==online
acceptance still PASSES, tsetlin tests green, live gauntlet 5/5.
HONEST TRADE-OFF, recorded rather than hidden: on the 5 real DrussGT
wave-surfing captures the GF family REGRESSES - GuessFactor 108 -> 55,
DecayGF 108 -> 76, KNN 101 -> 74 hits per 2000. The linear base is a poor
model for a surfer, so the residual histogram is noisier than the old
total-lead histogram. Linear itself improved there (95 -> 105). The synthetic
range and the live gauntlet both improved, and the structural bug is provably
fixed, so this was judged worth the cost - but recovering the DrussGT
regression is the next job, not something to wave away.
Wave queues (guess_factor, decay_gf, knn_gun): predict() stored ONE wave
per tick while onResult() popped one per resolved bullet (~4/tick), so the
queue drained to empty within a few dozen ticks, ~3 of every 4 resolutions
returned without learning, and the survivor paired with a same-tick wave
(bearingDelta ~= 0) pinning the histogram at centre. PROOF: GF.vHits ==
HeadOn.vHits and DecayGF.vHits == HeadOn.vHits byte-for-byte in every one
of 50 rounds — the guns had degenerated to HeadOn.
Now each gun keeps a per-bin FIFO with an O(1) head cursor. At most one
push per (tick, bin) so the fire site's 5th predict() call is a no-op, and
onResult pops the oldest wave of its OWN bin via e.bulletPower. Aiming
math untouched (it was already correct: 0 deg = East, CCW+).
maxBullets 2048 -> 8192: the rack spawns 52 bullets/tick so the ring wrapped
every ~39 ticks while a long power-3 shot needs ~90, silently discarding
unresolved bullets and biasing every measured hit rate by range. Added a
droppedBullets counter so a future overflow is measurable, and wavePushes/
waveStarved counters on the three guns. After the fix: vDropped = 0 and
vStarved = 0 across all 48 recorded rounds.
fitnessFor is now exported, deterministic (enemies iterated in ascending id
order) and shared by the selector and the stats dump, replacing a hand-rolled
merge in ModularBot that never advanced its window head.
Round lines gain additive keys: vDropped, vStarved.
- 30-tick cooldown after ghost-stuck/timeout ram exit prevents re-entry loop
- enemy_tracker.update() skips dead bots to prevent same-tick scan resurrection
- TFIL graphics cleared when ramming is active movement
- [config] logs: white base with green-highlighted changes only
- [ram:enter] logs trigger reason and key values on false→true transition
- [death] and [target-invalid] logs retained for diagnostics
- Cold-start bug: uniform bins[0..30]=0.1 made peakBin() always return
0 (first-wins tie), giving GF=-1 (max CW escape) before any learning.
Fixed with a triangular head-on bump at bin 15 (GF=0) as the prior.
- onResult now recomputes mea from FeedbackEvent.bulletPower instead of
the stale first-bin mea cached by predict; correct per-power-bin GF.
- Add DebugGF const (default false) with [gf-dbg] echoes in predict/onResult.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
SirStone