Commit Graph

2 Commits

Author SHA1 Message Date
SirStone 89370008da fix(tsetlin): make the TM actually learn - saturation 714 -> 13.8 literals/clause
The gun has never contributed anything: Tsetlin.vHits was byte-for-byte
equal to Linear.vHits in every measured round of every run, because its
learned correction was always exactly 0.

Six diagnosed defects fixed, plus one that was required to make the first
one work:

1. Type I now conditions on the clause output. It previously rewarded
   included true literals unconditionally, omitting Granmo's (c=0, lk=1)
   -> toward Exclude counter-force, so true literals ratcheted toward
   Include forever. This was the root cause of the saturation.
2. Type II was unreachable dead code: its guard required cOut==1 AND
   lits[lit]==0 AND st>0 (included), but cOut==1 guarantees every included
   literal is 1. Its direction was wrong too - it should increment EXCLUDED
   false literals when the clause fires.
3. Resource allocation restored: Granmo's (T - clip(v,-T,T))/(2T) target
   replaces |error|/(2*RESID_MAX); TM_T was only an output normaliser.
4. Label baseline fixed - the factor-2 shrink. predX = linearX + cx, so the
   label was delta - cx while the learner's output IS cx, giving
   error = delta - 2cx and a fixed point of cx = delta/2: HALF the needed
   correction even with perfect feedback. TmTrace now stores linearX/linearY
   and training uses delta.
5. Hits no longer zero their label (a hit means |miss| < 18px, not 0).
6. The enemy-energy feature was duplicated - tmEncodeFrame passed
   state.selfEnergy with a stale comment claiming enemyEnergy was absent,
   while WorldState.enemyEnergy exists. Enemy-energy rules were literally
   unrepresentable.
7. REQUIRED EXTRA: tmEvalClause now implements Granmo Eq. 6 - an all-Exclude
   clause outputs 1 during learning and 0 during classification. Without it,
   fix #1 deadlocks every clause at empty.

MEASURED EFFECT (energy-threshold-turner fixture, seed 1):
  mean included literals per active clause   714.0 -> 13.8
  active clauses                             100/100 -> 53/100
  nonzero corrections                        8/764 -> 708/764
  Tsetlin virtual hits (Linear = 27/400)     27/400 -> 69/400

Divergence achieved: offline on 7/8 fixtures, and in a live gauntlet
(RandomMover: Tsetlin 199/1200 vs Linear 288/1200, vDropped=vStarved=0).
Tsetlin now LEARNS but is not yet competitive with Linear - the regression
head is untuned, flagged as follow-up rather than claimed as a win.

Also ignores compiled test harnesses that have no file extension, which the
existing '**/tests/test_*' rule misses.
2026-09-20 23:59:32 +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