104 lines
5.4 KiB
Markdown
104 lines
5.4 KiB
Markdown
# bitbrain_gun — quick recap (inputs / outputs)
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Recap card. Everything below is read off `common_libs/guns/bitbrain_gun.nim`.
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## What it is today
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- A **lead-gain corrector on top of Pattern's prediction**. It scales Pattern's lead over the line of sight by a learned gain.
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- The **ADE/SBC neural network is REMOVED from the gun**. The name still says "BitBrain", but there is no net. The generic `common_libs/bitbrain/` library still exists and is tested separately (`test_bitbrain.nim`).
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## INPUTS
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| Input | Source in code |
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|---|---|
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| World state (self pos, enemy pos, tick) | `WorldState state` |
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| **Base aim** — Pattern's prediction | `g.tmh.pattern.predict(state, bulletSpeed)`: `TmHorizonGun.pattern`, a `PatternMatcherGun` (`guns/pattern_matcher`) |
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| Range band (5 bands: 0/100/200/300/450) | `bbBandOf(dist)`, `BB_BAND_LO/HI` |
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| **Gate** — gain applies only from band 3 up | `BB_GAIN_BAND_MIN = 3` → range **≥ ~300 px** |
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| **Label / feedback** — deferred observation lookup | at fire time store lead + tolerance; `h = round(dist/speed)` ticks later `tmhObservedAt(g.tmh, state.tick, selfX, selfY)` returns the enemy's OBSERVED bearing |
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| Aim tolerance (target's angular half-width) | `bbTolDeg(dist) = atan(18/range)` in degrees |
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| Config knobs | env, resolved once in `initBitBrainGun` (see table) |
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## OUTPUTS
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| Output | Formula / meaning |
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|---|---|
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| Aim point | `aim = LOS + gain * (patternAim - LOS)` — applied as angular `shift = (gain - 1.0) * lead` deg via `tmhApplyShift`; when `gain == 1.0` the base prediction is returned unchanged |
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| `gain` | argmax **hit rate** per band over the candidate list; `BB_CAND` default has **5 candidates** `{0.0, 0.25, 0.5, 0.75, 1.0}` (0 = HeadOn, 1 = Pattern); one candidate = fixed gain, no learning |
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| `[bb]` log line (only if `TR_BITBRAIN_LOG=1`; emitted only when `(gain, band)` changes) | see below |
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| Does **NOT** output | a predicted angle / bearing. It never aims on its own — it only rescales Pattern's lead. |
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`[bb]` fields, one at a time:
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| field | meaning |
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|---|---|
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| `t` | current tick |
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| `band` | lower edge of the range band in use (e.g. `300+`) |
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| `gain` | the gain being applied to Pattern's lead |
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| `shift` | angular shift actually applied = `(gain-1)*lead`, degrees |
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| `rate` | hit rate of the chosen gain in this band |
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| `n` | resolved samples in this band |
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| `ncand` | number of candidate gains |
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| `trained` | total resolved samples this battle |
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| `pend` | deferred labels still waiting |
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| `dropped` | labels that could not be resolved (stale / out-of-order) |
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| `mode` | memory mode: perRound / retained / decay |
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## KNOB TABLE (`TR_BITBRAIN_*`)
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**LIVE** — the resolved field is read by the learner/predict path:
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| Env | Meaning |
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|---|---|
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| `TR_BITBRAIN_GAINS` | comma-separated candidate gains (replaces `BB_CAND`) |
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| `TR_BITBRAIN_MEM` | perRound / retained / decay memory |
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| `TR_BITBRAIN_MIN_OBS` | samples before a band is trusted (in `bbGain`) |
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| `TR_BITBRAIN_DECAY` | decay interval in resolved samples (`resolvePending`) |
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| `TR_BITBRAIN_DECAY_FRAC` | per-decay shrink of the hit counts (`bbApplyDecay`) |
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| `TR_BITBRAIN_LOG` | `1` = emit the `[bb]` line |
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| `TR_BITBRAIN_RESET_ON_TARGET` | wipe learning when the enemy id changes (`targetChanged`) |
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**INERT** — kept only so old configs and the boot report don't warn; never touch the gain learner:
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| Env | Stored as (only read by boot report / guard test) |
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|---|---|
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| `TR_BITBRAIN_N` | `nClasses` |
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| `TR_BITBRAIN_NADE` | `nAde` |
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| `TR_BITBRAIN_RANGE` | `maxDeg` |
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| `TR_BITBRAIN_WARMUP` | `warmupN` |
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| `TR_BITBRAIN_ADAPT` | `adaptEvery` |
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| `TR_BITBRAIN_CALIB` | `calibEvery` |
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| `TR_BITBRAIN_SEED` | `seed` |
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*(Also `TR_RACK_BITBRAIN` — the rack admission switch, see below.)*
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## HOW TO TURN IT ON
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Default is **off**; the shipped rack never calls it. Minimal `.env`:
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```
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TR_RACK_BITBRAIN=both
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TR_RACK_PATTERN=off
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TR_BITBRAIN_GAINS=1.0 # 1.0 = identity = aims EXACTLY like Pattern
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TR_BITBRAIN_MEM=decay
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TR_BITBRAIN_LOG=1
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```
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⚠️ **`TR_BITBRAIN_GAINS=1.0` is the identity** — with one candidate at 1.0 the gun aims **exactly like Pattern**.
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**Do not read the candidate list as a recommendation.** Measured live: fixed gains **above** 1.0 are
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*decisively harmful* (1.5 → −93.8 dmg/run, p=0.0006; 1.25 → −34.2 dmg/run), a learner restricted to **≤ 1.0**
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is a **wash** (−2.5 dmg/run, p=0.87), and zero lead (= HeadOn) is **catastrophic** (14 vs 279 dmg/run).
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So the default set `{0, 0.25, 0.5, 0.75, 1.0}` is **not** a recommendation either — it merely *allows* the gun
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to shrink the lead toward HeadOn. Multi-candidate lists are for running the experiment, not for playing.
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## MEASURED VERDICT
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- **Neutral vs Pattern** across many opponents: damage/run 214.5 vs 210.9, round wins 49.4% vs 49.8% over 32 opponents (`docs/gauntlet_bitbrain_vs_pattern.md`).
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- **Specifically worse on DrussGT** alone: −18.1 damage/run (`docs/gauntlet_bitbrain_vs_pattern.md`, `docs/bitbrain_gun_verdict.md`).
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- The **lead-amplitude (gain) axis is CLOSED** — nothing beats Pattern in either direction (`docs/bitbrain_campaign.md` §Phase 2 / §2.6).
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## PROVENANCE
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- **Derived from code (this file):** what it is today, all inputs, the output formula, the `[bb]` fields, the LIVE/INERT split, and the "how to turn it on" env lines.
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- **Taken from the named evidence docs:** the numbers in MEASURED VERDICT above — see `docs/gauntlet_bitbrain_vs_pattern.md`, `docs/bitbrain_gun_verdict.md`, `docs/bitbrain_campaign.md`.
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