The selection thresholds were calibrated for a rate scale that does not exist.
MEASURED on an exact offline replay of a fogged live WorldState vs DrussGT
(1397 selection ticks), the 0.10 absolute floor fires on 53.0% of point-metric
ticks and forces HeadOn, which has a REAL hit rate of 2.0-4.4% - worst or
near-worst of 13 guns. HeadOn's selection share: 69.1% (abs+point) -> 43.5%
(rel+point). My earlier claim that the floor fires ALWAYS is REFUTED - it is
53%, because bestRate is a max over gun x bin and a >=50-sample bin
occasionally clears 10%. The mechanism is confirmed; the literal statement was
not.
Scale-aware mode (GUN_SELECTOR_MODE, absolute|relative, default relative):
RelTieMargin = 0.20 dimensionless FRACTION of bestRate, replacing the
fixed 2pp band so the band scales with the metric
FloorPeakFrac = 0.25 the floor fires iff bestRate < 0.25 * peakRateRef,
SelectorWindow = 256 where peakRateRef is the field-best rate over the last
256 selection ticks - keeping the original 'don't trust
a collapsed field' purpose but only when the field is
bad RELATIVE TO ITS OWN RECENT BEST, and counting only
guns with >= MinObsBeforeCompete samples so cold-start
100% spikes cannot pin HeadOn
also pools the rate over power bins instead of taking the max over bins, so
one lucky bin no longer wins
absolute mode is preserved byte-for-byte for rollback.
A/B vs DrussGT, real server hit rate, 3 runs x 10 rounds per config, one frozen
binary:
absolute+point 3.66 / 2.45 / 5.01 pooled 3.76%
absolute+path 7.55 / 8.21 / 6.83 pooled 7.57%
relative+point 7.66 / 6.18 / 5.79 pooled 6.59%
relative+path 7.15 / 7.55 / 6.90 pooled 7.21%
absolute+point is SEPARATED from all three (p < 0.0001); the other three
OVERLAP each other (p = 0.18-0.64). So the METRIC is the dominant lever and
under path the two threshold models are statistically tied.
DEFAULT SET: metric = path, thresholds = relative. absolute+path was nominally
0.35pp higher but indistinguishable (p = 0.64); relative is the principled
scale-aware fix, is the only model that works under BOTH metrics, and prevents
the point-metric catastrophe if anyone switches back. Shipping absolute would
ship the accidental side-effect this work exists to remove.
STILL NOT SOLVED: the selector remains only a moderate ranker.
Spearman(virtual rank, real rank) is 0.52 for the winning config, 0.36 pooled
for path and 0.04 for point - and it is INCONSISTENT across run sets. The
metric switch won by de-selecting HeadOn, not by ranking guns better. That is
the next problem.
TASK B, report only: do NOT drive selection from raw real hit rates yet.
Only the selected gun fires, so unselected guns get near-zero real shots
(GuessFactor 20, Linear 24 vs HeadOn 733); noise is fatal (n=470 at p=10% gives
+/-2.8pp, most guns n<200 gives +/-5pp+ across a 3-15% spread); and real rate is
conditional on when the gun was selected. A blended signal with forced
exploration and shrinkage is defensible in principle but needs thousands of
shots per gun across many battles. Real rate is best used OFFLINE as the
evaluation metric - which is exactly what this A/B did.
RELATED BUG FLAGGED, not fixed: MinHitRate = 0.40 in bestPower is on the same
wrong scale - no bin ever clears 40%, so once every bin has data, power
selection falls back to bin 0 (power 1.0) late in a round.
Verified: 33/33 guard checks, 11/11 metric checks, tsetlin green, 12/12
offline==online acceptance under the shipped default, run_range rc=0 over 20
fixtures. Adds analyze_selector.nim to measure floor/tie/bestRate/HeadOn-share
per config on any fixture.
Adds GUN_VBULLET_METRIC (point|path, default point = unchanged behaviour) so
the virtual-bullet hit model can be selected at runtime with no rebuild. Both
the live tracker and the offline replay read the same value, so the 12/12
offline==online acceptance holds under EITHER setting (verified for both).
A/B AGAINST THE LIVE BOSS, real server-side hit rate as ground truth, 5
battles x 12 rounds per metric on one frozen binary:
point 4660 shots / 219 hits = 4.70% (per-run 3.16-5.53)
path 4834 shots / 359 hits = 7.43% (per-run 6.55-8.24)
The distributions DO NOT OVERLAP: path's worst run beats point's best run.
+2.73pp, +58% relative, z = 5.56, p < 0.0001. Range distributions were
identical (~460-478 px), so this is not a range confound.
MECHANISM - and this is the important part. The gain is SELECTION, not better
gun learning. Under the point model every gun's virtual rate is compressed
into 0.6-4.4%, so HeadOn sits inside the 2pp tie margin and takes 72.6% of
selection ticks / 76.9% of shots - while HeadOn is 11th of 13 by REAL hit rate
(2.3%). The path model widens the band to 4.7-13.7% and ranks HeadOn 10th, so
its shot share falls to 35.9% and Pattern/Accel/WallBounce get picked instead.
Counterfactual: applying the point model's per-gun real rates to the path
model's shot mix yields 7.65%, i.e. essentially the whole observed gain.
So the selector, not the guns, is where the win lives.
PER-GUN REAL HIT RATE vs DrussGT (path mix, the answer to 'which guns are
worth keeping'): WallBounce 10.8, Pattern 10.5, Accel 10.0, Displace 9.3,
Circular 9.2, AvgLead 8.5, KNN 5.7, StopShot 5.2, GuessFactor 3.7,
Tsetlin 2.9. Per-gun N is small (hundreds of shots) so single-gun ordering is
indicative, not definitive.
TWO CAVEATS, recorded because they undercut a naive reading:
1. One adversary. DrussGT is a wave surfer and HeadOn is genuinely bad against
surfers, so part of this may be matchup-specific.
2. The path model is NOT a better general ranker. Spearman(virtual rank, real
rank) is 0.52 under point vs -0.04 under path. It wins by accidentally
fixing HeadOn's mis-rank, not by ranking guns better. A more durable fix is
to address the selection logic directly - which is the next job.
Also adds a focused guard test (test_vbullet_metric) covering parsing/default,
a receding-target point-miss/path-hit, a perpendicular-target path-miss, and
replay determinism.
Verified: 33 guard checks, 12/12 acceptance under both metrics, tsetlin tests
green, range 34.3% (point, unchanged) / 50.8% (path).
Gun evaluation previously required a full end-to-end battle (Java server +
battle runner + websocket IPC to 2 bot processes, 50 rounds, ~3.4 min) and
yielded only ~300-900 REAL shots across 13 guns -- far too few to rank
guns, which is why tuning needed many repetitions.
VirtualTracker is already a pure function of (WorldState stream, gun list);
the only reason it needed Java was where WorldState came from. So the range
replays a seq[WorldState] through the SAME tracker: offline and online
scores are the same metric by construction, not an approximation.
ACCEPTANCE TEST (the point of the whole thing): record one live round, replay
it offline, compare per-gun virtual hit rates. 12/12 deterministic guns match
EXACTLY, reproduced twice. Tsetlin is compared separately because tmLearnOne
calls rand(). Getting to 12/12 exposed two real ordering quirks in the live
loop: run() calls go() before the aim/fire block, so tickBullets resolves
against the NEXT tick's scan while the prediction used the previous one; and
if the target dies during that go() the final tick's spawn+resolution is
skipped entirely. The recorder emits an end marker for the second case.
The 5th (selected-gun) predict call was verified to be a no-op.
Measured cost: 8 fixtures (1770 ticks, ~92k virtual bullets, 13 guns) replay
in 2.9 s, ~32k virtual bullets/s -- roughly 70x faster and 100x more samples
than a live gauntlet.
Also adds a per-tick WorldState recorder behind const RecordWorldState
(default off, mirrors the ShotLog idiom) which records the state the bot
ACTUALLY builds, staleness included, rather than true positions -- recording
the latter would hand the guns perfect information and produce flattering
scores.
9 new guard checks (33 total, all passing), including fixture round-trip,
replay determinism, stationary->HeadOn 100%, constant-velocity->Linear>HeadOn,
and the energy-threshold turner crossing at t=41.
Measured, not assumed. With the gate temporarily opened to 20 deg, every
real shot was logged (tick, angle error at fire time, distance, power,
hit) across 3 gauntlets: 2611 shots, 57.3% aggregate. Findings:
- The geometric cone atan(BotRadius/d) is directionally confirmed but a
WEAK lever: even at 0.0-0.1 deg error the hit rate at 400-600px is only
~53-57%, because PREDICTION error dominates alignment error.
- Real effect of tightening the gate: 57.9% -> 68.0% aggregate hit rate
(fixed 0.1 deg), not the 76.9% previously reported -- that was a
high-variance draw (per-rep 62.8/66.4/77.2%).
- The shipped range-aware gate (SafetyFactor 0.6) does NOT beat the fixed
2.0 deg gate on hit rate (55.8% vs 57.9%, ~1.5 sigma, inside noise). It
fires 22-28% more shots and therefore lands more total hits (~509 vs
~434 per rep). No per-adversary score delta exceeded the 300-point
run-to-run noise band, so no config is demonstrably better on score.
Shipped anyway because it is strictly more expressive (a fixed threshold is
the special case), tunable from one const, and physically motivated, but
the honest verdict is recorded in-code: the gate is not the bottleneck.
AimThresholdDeg is removed; shouldFire now takes distPx. Degenerate or NaN
distance falls back to the ceiling rather than dividing by zero.
Also adds a per-shot logger to ModularBot behind 'const ShotLog' so the
measurement above is reproducible, and 10 new guard checks (24 total, all
passing) covering monotonicity, clamping, formula, perfect alignment,
gross misalignment and degenerate distance.
Cross-checked against the server source: the gun fires BEFORE the turn is
applied, so the logged angle error is the true departure error, and
fireAssist auto-aim is off (unset by the Nim API and forced false by
setAdjustRadarForGunTurn).
Four guns cached a whole prediction per tick while predict() is called once
per power bin, so every bin after the first (and the real fired shot, which
shares lastState) reused the power-1.0 lead. Fixed by caching only the
speed-INDEPENDENT derived state and recomputing the lead per requested speed:
- stop_shot: also fixes prevSpeed being written before it was read, which
made abs(speed) < abs(prev) permanently false and the entire
stop-prediction branch unreachable (it was just Linear).
- displacement: the cache key included bulletSpeed, so the guard missed on
all four bins and the 15-tick window advanced ~4x/tick, making the
inferred velocity ~4x too small.
- averaged_lead: tick cache removed outright. pattern_matcher: split into
speed-independent match+path and per-call lead.
FeedbackEvent gains fireTick/powerBin (additive; only virtual_bullets
constructs one) so guns can pair feedback to the exact shot instead of
guessing by coordinates. tsetlin uses it: traces are now keyed exactly by
(fireTick, powerBin) with a 1024-slot ring, and the 10-frame window shifts
at most once per tick (it was shifting ~4-5x/tick, so isWarmedUp tripped
after ~2 ticks).
KNOWN INCOMPLETE: tsetlin still does not diverge from Linear in battle. The
two named bugs are fixed (a 600-tick sim shows trainedShots=2141,
traceMisses=0, and a fixed-input probe converges to a 9.6px correction), but
the TM's clause feedback itself is broken: ~131 of 1740 literals end up
included per clause, so its conjunction never fires. Sweeping TM_S,
TM_N_CLAUSES and a two-branch Type-I update did not change the correction
from 0. Needs a real TM fix or removal, not another bug fix.
First-ever guard tests for the gun selector: common_libs/tests/
test_gun_harness.nim (14 checks, headless, no Java). There were none before,
which is how six broken guns survived a full analysis cycle. Against the
previous HEAD, 5 of these checks FAIL - that is the regression guard.
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.
Attribution is proven, not guessed: the server assigns a per-round-unique
bulletId (GunEngine.nextBulletId) and stamps the same id on BulletFired,
BulletHitBot, BulletHitWall and BulletHitBullet. Keep a FIFO of fired gun
ids, stamp bulletId -> gunId on onBulletFired, resolve through that map.
Hits are deferred when onBulletHit precedes onBulletFired in the same
turn (client dispatches priority 70 > 60), which recovered 14
unattributed hits. 99.9% of shots and 99.8% of hits attributed.
Stats lines now carry per-gun realShots/realHits/realHitRate; the old
keys and round-level totals are unchanged.
bestPower: a gun with zero observations in every bin previously returned
the HIGHEST bin (power 3.0) because an empty bin satisfied the
'count == 0' clause on the first countdown iteration. Cold guns now
return the lowest bin as the docstring always claimed. Warm-gun path
untouched.
- bestGun: replace first-index-wins argmax with random pick among guns
within TieMargin (2%) of best rate. HeadOn at index 0 was silently
winning every tie, starving Tsetlin/Linear/etc.
- MinObsBeforeCompete 15 -> 50 (Pattern entered competition on noise)
- add MinHitRateFloor 0.10: if no gun clears it, fall back to HeadOn
instead of selecting the best of a bad field
- ModularBot: remove AntiSurfer gun (0% virtual hit rate everywhere),
14 -> 13 guns, renumber ids and selection counters
Gun selector now gates competition: guns with <15 total observations across
all power bins sit out until at least one gun qualifies. Falls back to ungated
selection if no gun reaches threshold, preventing cold-start stalls.
Sliding window increased from 50 to 100 ticks to reduce switching noise.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
SirStone