SirStone
974528d5cf
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.
43 lines
1.5 KiB
Nim
43 lines
1.5 KiB
Nim
## Shared helper: build the 13 ModularBot guns as type-erased offline range
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## drivers, in ModularBot's gun-id order, with the same readiness gate the live
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## loop uses (Tsetlin only spawns once its 10-frame window is full).
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import std/random
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import gun_harness/offline_range
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import guns/head_on
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import guns/linear
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import guns/circular
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import guns/tsetlin
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import guns/guess_factor
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import guns/pattern_matcher
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import guns/wall_bounce
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import guns/accel_predictor
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import guns/stop_shot
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import guns/displacement
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import guns/averaged_lead
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import guns/decay_gf
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import guns/knn_gun
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proc buildAllGunDrivers*(seed = -1): seq[GunDriver] =
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## seed >= 0 re-seeds the global RNG after constructing Tsetlin so the
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## stochastic gun's learning is reproducible for offline runs. (Its
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## constructor calls randomize(); we override that seed afterwards.)
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var tsetlin = initTsetlinGun()
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if seed >= 0:
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randomize(seed)
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result = @[
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makeDriver("HeadOn", HeadOnGun()),
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makeDriver("Linear", LinearGun()),
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makeDriver("Tsetlin", tsetlin),
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makeDriver("Circular", CircularGun()),
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makeDriver("GuessFactor", initGFGun()),
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makeDriver("Pattern", PatternMatcherGun()),
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makeDriver("WallBounce", initWallBounceGun()),
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makeDriver("Accel", initAccelGun()),
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makeDriver("StopShot", initStopShotGun()),
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makeDriver("Displace", initDisplacementGun()),
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makeDriver("AvgLead", initAveragedLeadGun()),
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makeDriver("DecayGF", initDecayGFGun()),
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makeDriver("KNN", initKNNGun()),
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]
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