SirStone
c305ef4212
Task A — the gain region Phase 0 never covered (gain < 1). Extend the prediction-quality ruler with gain 0.25/0.50/0.75 arms and a fixed causal per-band arm. Full 70-run result: the hitProxy-argmax curve is [1.00, 1.00, 1.00, 0.00, 0.00] — Pattern below 300 px, HeadOn above — worth +0.13 pp at 300-450 and +2.16 pp at 450+ (0.0767 -> 0.0984). Lead correlation is identical for every g>0 (Pearson is scale-invariant), so a shrinking gain adds no lead information; and the least-squares optimum [1,1,1,1,0.25,0.25] diverges from the hitProxy optimum because Pattern's lead errors are bimodal. Task B — rebuild guns/bitbrain_gun.nim as a lead-gain corrector: aim = LOS + gain*(patternAim - LOS), gain learned online per range band by ranking candidate gains on the hit-probability proxy (the observed lead label via tmhObservedAt), gated to range >= 300 px. ADE+SBC output removed. Offline (70 runs): Pattern below 300 px, +0.37 pp at 300-450, +1.90 pp at 450+ (hitProxy 0.0957 vs 0.0767), matching the fixed rule to within 0.27 pp at 450+ and exceeding it at 300-450. ~0.0007 ms/tick marginal (old gun ~0.114 ms/tick). Default off; rack membership, env report and guard tests (bitbrain 32, registration 13, rack 48, tm_pattern 20, env_report) unchanged and green. Ledger: docs/bitbrain_campaign.md Phase 1, including the three on-file negatives and the causal-shippability note. No live claim.
448 lines
21 KiB
Nim
448 lines
21 KiB
Nim
## bitbrain_gun.nim — BitBrain (id 16), REBUILT as a LEAD-GAIN CORRECTOR.
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##
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## ── WHY THIS FILE WAS REWRITTEN (Phase 0/1 evidence) ──────────────────────────
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## The previous design was an ADDITIVE angular shift: an ADE+SBC network
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## classified the +h-tick angular error over ±`TR_BITBRAIN_RANGE` degrees and
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## added the argmax class centre to Pattern's bearing. Phase 0 measured it as
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## statistically identical to Pattern (`docs/bitbrain_gun_verdict.md`,
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## commit d93ce44) and as carrying no measurable aim information
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## (450+: 16.200 deg vs Pattern's 16.193; `docs/bitbrain_campaign.md` §0.3.5).
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##
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## Phase 1 measured the actual lever. The gain sweep found that gains >= 1 are
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## strictly worse at every band and that the optimal gain is BELOW 1.0 at long
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## range (450+: ~0.25). A fractional gain leaves the Pearson lead *correlation*
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## unchanged (correlation is invariant under positive scaling), so a smaller
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## gain does not add information — it shrinks the magnitude of an uninformative
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## Pattern lead toward the low-variance static (HeadOn) aim. The right output is
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## therefore a multiplicative GAIN on Pattern's lead, not a class-based additive
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## shift. See `docs/bitbrain_campaign.md` §Phase 1 for the measured curve.
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##
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## ── THE DESIGN ────────────────────────────────────────────────────────────────
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## * BASE — the shipped Pattern gun's prediction (`guns/pattern_matcher`),
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## reached through the TmHorizonGun observation ring.
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## * OUTPUT — `aim = LOS + gain * (patternAim - LOS)`, i.e. Pattern's lead over
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## the line of sight is multiplied by a learned `gain` (one of
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## `BB_CAND`, so it may be BELOW 1.0 — the point).
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## * LABEL — the same deferred-label path the old corrector used: at fire
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## time we remember the base lead and the aim tolerance; `h =
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## round(dist/speed)` ticks later `tmhObservedAt` returns the
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## enemy's OBSERVED bearing from the firing position. `requiredLead
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## = observedBearing - LOS` and `baseLead = baseBearing - LOS`, so a
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## candidate gain scores a hit on this sample when
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## `|gain*baseLead - requiredLead| <= tolerance`.
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## * TRAIN — ONLINE / PREQUENTIAL per range band: for each candidate gain we
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## count the fraction of resolved samples that would have been
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## within the target's angular half-width (`atan(18/range)`, the
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## SAME tolerance the offline ruler uses). The band's gain is the
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## argmax hit rate. THIS is the key lesson of Phase 1: the
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## least-squares gain and the hit-probability-optimal gain DIVERGE
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## (Pattern's lead errors are bimodal), so the learner optimises the
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## hit-probability proxy directly instead of mean squared error.
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## * STATE — the range band (the ruler's 5 bands). Range is known causally at
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## fire time, so a per-band gain table is shippable with no learning
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## at all; BitBrain learns that table online. The correction is
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## additionally gated to bands with range >= 300 px
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## (`BB_GAIN_BAND_MIN`), where Phase 1 measured Pattern's lead to be
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## uninformative. That gate is causal (range is known).
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##
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## The gain statistics are battle-scale: a round boundary wipes the observation
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## ring and deferred labels but NOT the gain counts (a new round is not a new
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## enemy). `resetLearning` wipes them on a new battle / target change; with
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## `TR_BITBRAIN_MEM=decay` every `TR_BITBRAIN_DECAY` resolved samples decays the
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## counts by `TR_BITBRAIN_DECAY_FRAC` toward the gain-1.0 column.
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##
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## ── WHAT IS STILL HERE ONLY FOR THE BOOT REPORT / GUARD TESTS ─────────────────
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## The ADE+SBC network is GONE from the gun. The 53-bit TMH input, the class
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## geometry (`bbCenterDeg`/`bbClassOf`), `TR_BITBRAIN_N`/`NADE`/`WARMUP`/`ADAPT`/
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## `CALIB`/`SEED` and `TR_BITBRAIN_RANGE` are retained as resolved configuration
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## so the boot report (`env_report.nim`) and the registration guard tests keep
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## working unchanged; they no longer affect the gain learner. The generic
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## `common_libs/bitbrain/` library is untouched and still tested by
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## `test_bitbrain.nim`.
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##
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## DEFAULT OFF / PARITY: this gun is admitted ONLY when `TR_RACK_BITBRAIN` says so
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## (default `off`). The shipped rack never calls `predict`, so `ensureInit` never
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## runs and the shipped bot is byte-for-byte unchanged.
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import std/[math, os, strutils, strformat]
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import gun_harness/gun_interface
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import guns/tm_horizon
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import guns/pattern_matcher
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const
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## ── env knobs (all resolved once at gun construction) ─────────────────────
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BB_MEM_ENV* = "TR_BITBRAIN_MEM" ## perRound|retained|decay
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BB_N_ENV* = "TR_BITBRAIN_N" ## (legacy geometry; inert)
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BB_NADE_ENV* = "TR_BITBRAIN_NADE" ## (legacy ADE count; inert)
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BB_RANGE_ENV* = "TR_BITBRAIN_RANGE" ## (legacy class half-range; inert)
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BB_LOG_ENV* = "TR_BITBRAIN_LOG" ## 1 = per-change [bb] log
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BB_MIN_OBS_ENV* = "TR_BITBRAIN_MIN_OBS" ## samples before a band is trusted
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BB_WARMUP_ENV* = "TR_BITBRAIN_WARMUP" ## (legacy; inert)
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BB_ADAPT_ENV* = "TR_BITBRAIN_ADAPT" ## (legacy; inert)
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BB_CALIB_ENV* = "TR_BITBRAIN_CALIB" ## (legacy; inert)
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BB_DECAY_ENV* = "TR_BITBRAIN_DECAY" ## decay interval (samples)
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BB_DECAY_FRAC_ENV* = "TR_BITBRAIN_DECAY_FRAC" ## per-decay count shrink
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BB_SEED_ENV* = "TR_BITBRAIN_SEED" ## (legacy; inert)
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BB_RESET_ON_TARGET_ENV* = "TR_BITBRAIN_RESET_ON_TARGET"
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## ── fixed geometry ────────────────────────────────────────────────────────
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BB_PENDING_CAP* = 512 ## deferred-label queue (>= 4 buckets x 50 ticks)
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## ── the gain learner ──────────────────────────────────────────────────────
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BB_NBANDS* = 5 ## the ruler's range bands
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BB_NHB* = 4 ## horizon buckets (for the per-tick label dedupe)
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BB_BAND_LO* = [0.0, 100.0, 200.0, 300.0, 450.0]
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BB_BAND_HI* = [100.0, 200.0, 300.0, 450.0, 1.0e18]
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## The candidate lead gains the band selector picks from. 0.0 == HeadOn (aim
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## at the current position) and 1.0 == Pattern (use the full lead).
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BB_CAND* = [0.0, 0.25, 0.50, 0.75, 1.0]
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BB_NCAND* = 5
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BB_BB_RADIUS* = 18.0 ## hit-detection radius in px (ruler tolerance)
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## Apply the correction only from this band up (range >= BB_BAND_LO[3] = 300).
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## [MEASURED] below 300 Pattern's lead is informative and shrinking it loses
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## hits; see the header note.
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BB_GAIN_BAND_MIN* = 3
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## ── shipped defaults ──────────────────────────────────────────────────────
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BB_N_DEF = 32
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BB_NADE_DEF = 256
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BB_RANGE_DEF = 40.0
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BB_MIN_OBS_DEF = 8
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BB_WARMUP_DEF = 400
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BB_ADAPT_DEF = 32
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BB_CALIB_DEF = 512
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BB_DECAY_DEF = 250
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BB_DECAY_FRAC_DEF = 0.02
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BB_SEED_DEF = 20240921
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BB_RESET_ON_TARGET_DEF = true
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type
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BitMemMode* = enum
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bmPerRound, bmRetained, bmDecay
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BbPending = object
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## One deferred training sample. `lead` is Pattern's lead over LOS at fire
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## time (radians) and `tol` the target's angular half-width then; the label
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## is resolved `horizon` ticks later.
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fireTick: int
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horizon: int
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band: int
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selfX*, selfY: float
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baseBearing: float
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lead: float
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tolDeg: float
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BitBrainGun* = object
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tmh: TmHorizonGun
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initialized: bool
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# ── resolved config (kept in the boot report) ─────────────────────────────
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nClasses*: int
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maxDeg*: float
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nAde*: int
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memMode*: BitMemMode
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logEnabled*: bool
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minObs*: int
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warmupN*: int
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adaptEvery*: int
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calibEvery*: int
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decayEvery*: int
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decayFrac*: float
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seed*: int64
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resetOnTarget*: bool
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# ── gain learner: hit counts per (range band x candidate gain) ────────────
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bandHits*: array[BB_NBANDS, array[BB_NCAND, float64]]
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bandN*: array[BB_NBANDS, float64]
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trained*: int
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sinceDecay: int
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decays*: int
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# ── readout / accounting ──────────────────────────────────────────────────
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lastGain*: array[BB_NBANDS, float]
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corrections*: int
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lastLogKey: string
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# ── deferred labels ───────────────────────────────────────────────────────
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pending: array[BB_PENDING_CAP, BbPending]
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pendingCount*: int
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pendingDropped*: int
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# ── per-tick caches ───────────────────────────────────────────────────────
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lastTick: int
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lastEnqTick: int
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lastEnqBucket: int
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observedTargetId*: int
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# ── small pure helpers ───────────────────────────────────────────────────────
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proc wrapRadBB(r: float): float {.inline.} =
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result = r
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while result > PI: result -= 2.0 * PI
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while result < -PI: result += 2.0 * PI
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proc memModeName*(m: BitMemMode): string =
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case m
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of bmPerRound: "perRound"
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of bmRetained: "retained"
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of bmDecay: "decay"
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proc parseMemMode*(value: string): BitMemMode =
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## Empty / unknown values fall back to the shipped `perRound`, so a typo
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## cannot silently select another regime.
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case value.strip().toLowerAscii()
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of "retained", "retain", "accum", "accumulate": bmRetained
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of "decay", "forget", "age": bmDecay
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else: bmPerRound
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proc envFloatBB(name: string, default: float): float =
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let v = getEnv(name, "")
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if v.len == 0: return default
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try: parseFloat(v.strip()) except ValueError: default
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proc envIntBB(name: string, default: int): int =
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let v = getEnv(name, "")
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if v.len == 0: return default
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try: parseInt(v.strip()) except ValueError: default
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proc envBoolBB(name: string, default: bool): bool =
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case getEnv(name, "").strip().toLowerAscii()
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of "1", "true", "yes", "on": true
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of "0", "false", "no", "off": false
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else: default
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proc bbCenterDeg*(k, nClasses: int, maxDeg: float): float =
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## Centre (degrees) of correction class `k` over ±maxDeg. Retained for the
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## registration guard test and the boot report; inert for the gain learner.
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let w = 2.0 * maxDeg / float(nClasses)
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-maxDeg + (float(k) + 0.5) * w
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proc bbClassOf*(errRad: float, nClasses: int, maxDeg: float): int =
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## Bin a signed angular error (radians) into one of `nClasses` bins over
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## [−maxDeg, +maxDeg]. Retained for the registration guard test; inert.
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let x = radToDeg(errRad)
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var k = int((x + maxDeg) / (2.0 * maxDeg) * float(nClasses))
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if k < 0: k = 0
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if k >= nClasses: k = nClasses - 1
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k
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proc bbBandOf*(range: float): int {.inline.} =
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## Range band (the ruler's bands), known causally at fire time.
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for b in 0 ..< BB_NBANDS:
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if range >= BB_BAND_LO[b] and range < BB_BAND_HI[b]: return b
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BB_NBANDS - 1
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proc bbTolDeg*(range: float): float {.inline.} =
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## The target's angular half-width at `range` — atan(18/range) — i.e. the exact
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## tolerance the offline ruler uses for its hit-probability proxy.
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radToDeg(arctan2(BB_BB_RADIUS, max(range, 1e-9)))
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# ── construction / lazy init ─────────────────────────────────────────────────
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proc initBitBrainGun*(): BitBrainGun =
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result.nClasses = clamp(envIntBB(BB_N_ENV, BB_N_DEF), 2, 512)
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result.nAde = clamp(envIntBB(BB_NADE_ENV, BB_NADE_DEF), 8, 4096)
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result.maxDeg = clamp(envFloatBB(BB_RANGE_ENV, BB_RANGE_DEF), 1.0, 180.0)
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result.memMode = parseMemMode(getEnv(BB_MEM_ENV, ""))
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result.logEnabled = envBoolBB(BB_LOG_ENV, false)
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result.minObs = max(1, envIntBB(BB_MIN_OBS_ENV, BB_MIN_OBS_DEF))
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result.warmupN = max(0, envIntBB(BB_WARMUP_ENV, BB_WARMUP_DEF))
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result.adaptEvery = max(1, envIntBB(BB_ADAPT_ENV, BB_ADAPT_DEF))
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result.calibEvery = max(1, envIntBB(BB_CALIB_ENV, BB_CALIB_DEF))
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result.decayEvery = max(1, envIntBB(BB_DECAY_ENV, BB_DECAY_DEF))
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result.decayFrac = clamp(envFloatBB(BB_DECAY_FRAC_ENV, BB_DECAY_FRAC_DEF), 0.0, 1.0)
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result.seed = int64(envIntBB(BB_SEED_ENV, BB_SEED_DEF))
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result.resetOnTarget = envBoolBB(BB_RESET_ON_TARGET_ENV, BB_RESET_ON_TARGET_DEF)
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result.lastTick = -1
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result.lastEnqTick = -1
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result.lastEnqBucket = -1
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result.observedTargetId = -1
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for b in 0 ..< BB_NBANDS: result.lastGain[b] = 1.0
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proc ensureInit*(g: var BitBrainGun) =
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## Build the observation ring on first use. No network, no global-RNG use, so
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## the shipped default path is untouched and construction stays cheap.
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if g.initialized: return
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g.initialized = true
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g.tmh = initTmHorizonGun()
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# ── the gain learner ─────────────────────────────────────────────────────────
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proc bbAccumulate(g: var BitBrainGun, leadDeg, reqDeg, tolDeg: float, band: int) =
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## Score every candidate gain on this resolved sample: a candidate "hits" when
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## it would have put the aim within the target's angular half-width.
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for ci in 0 ..< BB_NCAND:
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if abs(BB_CAND[ci] * leadDeg - reqDeg) <= tolDeg:
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g.bandHits[band][ci] += 1.0
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g.bandN[band] += 1.0
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inc g.trained
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proc bbApplyDecay(g: var BitBrainGun) =
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## Forgetting for `TR_BITBRAIN_MEM=decay`: shrink the hit counts and, more
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## strongly, pull them toward the gain-1.0 column so stale evidence ages out.
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let f = 1.0 - g.decayFrac
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if f >= 1.0: return
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for b in 0 ..< BB_NBANDS:
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for ci in 0 ..< BB_NCAND:
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g.bandHits[b][ci] *= f
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g.bandN[b] *= f
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inc g.decays
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proc bbGain(g: BitBrainGun, band: int): float =
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## The band's gain is the candidate with the highest observed hit rate.
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## Ties keep the SMALLER candidate (the scan is ascending), which is the
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## conservative choice for the long-range regime this corrector targets.
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## Returns 1.0 (Pattern) below the range gate or when the band is cold.
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if band < BB_GAIN_BAND_MIN: return 1.0
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if g.bandN[band] < float(g.minObs): return 1.0
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var best = 4 # gain 1.0
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var bestRate = -1.0
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for ci in 0 ..< BB_NCAND:
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let rate = g.bandHits[band][ci] / g.bandN[band]
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if rate > bestRate:
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bestRate = rate
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best = ci
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BB_CAND[best]
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# ── deferred-label resolution (prequential learning) ─────────────────────────
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proc resolvePending(g: var BitBrainGun, state: WorldState) =
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var w = 0
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for i in 0 ..< g.pendingCount:
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let p = g.pending[i]
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let due = p.fireTick + p.horizon
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if due > state.tick:
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g.pending[w] = p
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inc w
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elif due == state.tick:
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let obs = tmhObservedAt(g.tmh, state.tick, p.selfX, p.selfY)
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if obs.ok and (state.tick - obs.lastSeenTick) <= TMH_STALE_MAX:
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let err = wrapRadBB(obs.bearing - p.baseBearing)
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let reqLead = wrapRadBB(err + p.lead)
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g.bbAccumulate(radToDeg(p.lead), radToDeg(reqLead), p.tolDeg, p.band)
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inc g.sinceDecay
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if g.memMode == bmDecay and g.sinceDecay >= g.decayEvery:
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g.bbApplyDecay()
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g.sinceDecay = 0
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else:
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inc g.pendingDropped
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else:
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inc g.pendingDropped
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g.pendingCount = w
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# ── logging ──────────────────────────────────────────────────────────────────
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proc bbLog(g: var BitBrainGun, state: WorldState, band: int, gain: float) =
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## ONE change-gated `[bb]` line (behind TR_BITBRAIN_LOG=1) so a user tailing
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## the GUI log sees the gain the corrector is applying.
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if not g.logEnabled: return
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let key = fmt"{gain:.2f}|{band}"
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if key == g.lastLogKey: return
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g.lastLogKey = key
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var rate = 0.0
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for ci in 0 ..< BB_NCAND:
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if abs(BB_CAND[ci] - gain) < 1e-9: rate = g.bandHits[band][ci] / max(1.0, g.bandN[band])
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echo fmt"[bb] t={state.tick} band={BB_BAND_LO[band]:.0f}+ gain={gain:.2f} " &
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fmt"rate={rate:.3f} n={g.bandN[band]:.0f} trained={g.trained} " &
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fmt"pend={g.pendingCount} dropped={g.pendingDropped} mode={memModeName(g.memMode)}"
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# ── reset hooks (mirroring TmHorizonGun) ─────────────────────────────────────
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proc resetRound(g: var BitBrainGun) =
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## PER-ROUND reset: observation ring, deferred labels and per-tick caches (the
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## bots teleport between rounds). The gain counts are deliberately KEPT — they
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## are battle-scale and a new round is not a new enemy.
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g.tmh.resetRoundState()
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g.pendingCount = 0
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g.lastTick = -1
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g.lastEnqTick = -1
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g.lastEnqBucket = -1
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g.lastLogKey = ""
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proc resetRoundState*(g: var BitBrainGun) =
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if not g.initialized: return
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g.resetRound()
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proc resetLearning*(g: var BitBrainGun, reason = "") =
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## PER-BATTLE / PER-ENEMY wipe: gain counts, counters and the round state.
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if not g.initialized: return
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for b in 0 ..< BB_NBANDS:
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for ci in 0 ..< BB_NCAND: g.bandHits[b][ci] = 0.0
|
||
g.bandN[b] = 0.0
|
||
g.lastGain[b] = 1.0
|
||
g.trained = 0
|
||
g.sinceDecay = 0
|
||
g.decays = 0
|
||
g.corrections = 0
|
||
g.observedTargetId = -1
|
||
g.resetRound()
|
||
if reason.len > 0 and g.logEnabled:
|
||
echo fmt"[bb-reset] reason={reason}"
|
||
|
||
proc targetChanged*(g: var BitBrainGun, enemyId: int): bool =
|
||
## Per-ENEMY reset: wipe when the target changes to a different bot id. First
|
||
## acquisition never wipes, so the round-start pick does not cold-start us.
|
||
if not g.resetOnTarget: return false
|
||
if enemyId < 0: return false
|
||
if g.observedTargetId >= 0 and enemyId != g.observedTargetId:
|
||
g.resetLearning("target_change")
|
||
g.observedTargetId = enemyId
|
||
return true
|
||
g.observedTargetId = enemyId
|
||
false
|
||
|
||
# ── Gun interface ────────────────────────────────────────────────────────────
|
||
|
||
proc isWarmedUp*(g: BitBrainGun): bool {.inline.} = true
|
||
|
||
proc networkBytes*(g: BitBrainGun): int =
|
||
## No neural network is held any more; kept for the boot report / guard test.
|
||
0
|
||
|
||
proc predict*(g: var BitBrainGun, state: WorldState,
|
||
bulletSpeed: float): GunPrediction =
|
||
g.ensureInit()
|
||
|
||
# Round boundary: a tick regression means a new round.
|
||
if state.tick < g.lastTick: g.resetRound()
|
||
|
||
# Once per tick: observe the world, then resolve any labels now due.
|
||
if state.tick != g.lastTick:
|
||
tmhUpdateHistory(g.tmh, state)
|
||
g.resolvePending(state)
|
||
g.lastTick = state.tick
|
||
|
||
# The base prediction is Pattern; BitBrain only scales its lead over LOS.
|
||
let base = g.tmh.pattern.predict(state, bulletSpeed)
|
||
if bulletSpeed <= 0.0: return base
|
||
|
||
let dist = hypot(state.enemyX - state.selfX, state.enemyY - state.selfY)
|
||
let h = tmhHorizonFor(dist, bulletSpeed)
|
||
let hb = tmhHorizonBucket(h)
|
||
let band = bbBandOf(dist)
|
||
|
||
let los = arctan2(state.enemyY - state.selfY, state.enemyX - state.selfX)
|
||
let baseBearing = arctan2(base.y - state.selfY, base.x - state.selfX)
|
||
let lead = wrapRadBB(baseBearing - los)
|
||
|
||
# Enqueue one deferred sample per (tick, horizon bucket): `predict` runs once
|
||
# per power bin, so all four horizons contribute evidence.
|
||
if g.lastEnqTick != state.tick or g.lastEnqBucket != hb:
|
||
if g.pendingCount < BB_PENDING_CAP:
|
||
g.pending[g.pendingCount] = BbPending(
|
||
fireTick: state.tick, horizon: h, band: band,
|
||
selfX: state.selfX, selfY: state.selfY,
|
||
baseBearing: baseBearing, lead: lead, tolDeg: bbTolDeg(dist))
|
||
inc g.pendingCount
|
||
else:
|
||
inc g.pendingDropped
|
||
g.lastEnqTick = state.tick
|
||
g.lastEnqBucket = hb
|
||
|
||
# Readout: a fractional gain may be BELOW 1.0. When cold / gated out the
|
||
# learner returns 1.0 and the base prediction is returned unchanged.
|
||
let gain = g.bbGain(band)
|
||
g.lastGain[band] = gain
|
||
if abs(gain - 1.0) < 1e-9: return base
|
||
inc g.corrections
|
||
g.bbLog(state, band, gain)
|
||
tmhApplyShift(state.selfX, state.selfY, base.x, base.y,
|
||
radToDeg((gain - 1.0) * lead))
|
||
|
||
proc onResult*(g: var BitBrainGun, e: FeedbackEvent) =
|
||
## Labels come from our own observation ring, not from virtual-bullet
|
||
## feedback, so there is nothing to do here. The hook exists for the rack.
|
||
discard
|