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Author SHA1 Message Date
SirStone 68e0375be2 feat(radar): adaptive melee radar sweeps only the arc containing all enemies 
Replaces melee_scan in the rack. melee_scan spun the radar at the 45 deg/tick
cap unconditionally, so a full 360 deg revolution took 8 ticks and every enemy
was scanned roughly every 8 ticks. The new module starts with the same full
spin, and once it is SURE it has covered every enemy it sweeps back and forth
over only the minimal covering arc of all enemy bearings.

MEASURED, real melees via the bridge, per-enemy onScannedBot counts:
  3-bot melee (2 enemies): 29.6 -> 61.1 scans/100 melee-ticks  (2.06x)
  4-bot melee (3 enemies): 36.0 -> 75.7 scans/100 melee-ticks  (2.10x)
Covering-arc widths observed: mostly <90 deg in the 2-enemy case, up to 240 deg
in the 3-enemy case, so the gain shrinks as the arc widens - and at the
ExitTrackWidthDeg=300 fallback it degenerates to exactly the old full spin, so
there is no loss when narrowing would not help.

TRADEOFF, recorded rather than hidden: a wider arc legitimately takes longer to
traverse, so the freshness window costs 5-7 points (fresh<=16: 93-95% vs
98-100%) and more at fresh<=8 (75-76% vs 97-100%). More scans per enemy, at
slightly staler individual fixes.

DESIGN: acquisition spins 360 until every live known enemy was seen within
FreshnessTicks=16 (two revolutions of slack), no new id appeared, and the live
count matches getEnemyCount(); that must hold FreshStreakTicks=3 consecutive
ticks. Tracking then bang-bang sweeps the wraparound-aware covering arc
(350+10 -> 20 through 0) widened by MarginDeg=20 each end, at up to 45 deg/tick.
Fallbacks return to acquisition: any stale enemy, any new id, or an arc >= 300
deg. Enter 270 / Exit 300 gives 30 deg of hysteresis so it cannot flap.

Adds EnemyInfo.lastSeenTick (additive) so coverage is judged on staleness, not
mere knowledge - without it an enemy that slipped behind the sweep would keep
contributing its own stale bearing, which is self-confirming. The offline range
now round-trips that field from the fixture 'lst'.

COMPANION FIX, and it matters: the radar-mode switch used the TRACKER's known
enemy count, so in melee the bot saw one enemy before scanning the second, locked
to 1v1, and the melee radar never ran at all. Now uses getEnemyCount() (server
truth), so melee mode persists until one enemy is genuinely left.

41 new unit checks (wraparound arcs, straddle at 0/360, single/empty enemies,
the 45 deg/tick cap, every phase transition and fallback). melee_scan is kept
but marked DEPRECATED; nothing in the rack imports it.

Non-regression: 33 gun-harness checks, vbullet metric, power selection, and
12/12 offline==online acceptance all pass.
 2026-09-21 21:35:22 +02:00
SirStone dea4dcb574 feat(gun_harness): scale-aware selector thresholds; default = path + relative 
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.
 2026-09-21 04:33:52 +02:00
SirStone 3b5d70b7c3 feat(gun_harness): runtime metric switch + A/B proving the point metric mis-selects 
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).
 2026-09-21 03:58:27 +02:00
SirStone 974528d5cf feat(gun_harness): offline gun range, proven equivalent to live play 
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.
 2026-09-20 23:44:42 +02:00