SirStone
c9825dfb0b
Implements the user's energy management request: "firing from more than 200px should be a 'not good chances zone' so faster bullets and more chances to hit matters more than single hit damage with low chances. When we are lower than 50 health, same thing. I would like to use 3.0 power only when the chances of hitting are higher than average." Design: a CAP on top of the existing `bestPower`, not a rewrite. `bestPower` still answers "which bin does this gun's own data prefer"; the policy caps it: ramming -> 3.0 (reason ram, exempt) dist > TR_POWER_FAR_DIST (200) -> 1.0 (far) elif selfEnergy < TR_POWER_LOW_ENERGY (50) -> 1.0 (lowEnergy) elif pEst <= pRef -> 2.0 (belowAvg) else -> 3.0 (full) power = min(gunPreferredBinPower, cap) # can only LOWER power p=1.0 is the right "low" tier on the measured mechanics: bullet speed 20-3p so p=1.0 gives speed 17 vs 11 at p=3.0 (55% faster = less lead error), fire interval 10+2p so 12 ticks vs 16 (33% more shots), and drain 0.083/turn vs 0.1875 (2.25x slower). All three things the user asked for at long range. pEst = the chosen bin's virtual rate (gun aggregate when the bin is empty); pRef = the gun's aggregate mean unless TR_POWER_REF > 0. No-data guns are vacuously below-average -> cap 2.0 (conservative, documented). Control arm: TR_POWER_POLICY=0 = uncapped = today's behaviour exactly. Knobs: TR_POWER_POLICY, TR_POWER_FAR_DIST, TR_POWER_LOW_ENERGY, TR_POWER_FAR_CAP, TR_POWER_MID_CAP, TR_POWER_REF, TR_POWER_LOG. TR_POWER_MID_CAP exists because the user did not specify the middle case (close + healthy + not-above-average); 2.0 is the default, flippable to 1.0. Seam: the cap lives in a pure `applyPowerPolicy` and is applied only in `selectShot` (the single place real shots are chosen), so the logic is testable without a battle. Ram is wired from `shouldRam` - the same value the movement dispatch uses for the (0,50) band. CORRECTION TO AN ASSUMPTION IN THE TASK: `offline_range.nim` does NOT call `bestPower`/`selectShot` - it only replays virtual-bullet spawn/resolve across all power bins, independent of the real shot's power. So there is no offline power-selection path that could diverge from the live one, and the acceptance test guards the metric, not the policy. Policy coverage therefore comes from the new unit test. Verification: test_power_policy 26/26 in BOTH modes (default and TR_POWER_POLICY=0 control arm); test_gun_harness 39, test_vbullet_metric 11, test_power_selection 3, test_adaptive_radar 41, test_tfil_ring_weights 24; acceptance_offline_vs_online 12/12 VERDICT PASS (live battle). ModularBot compiles. UNVERIFIED: the live effect on damage/survival/score. No A/B has run.
878 lines
39 KiB
Nim
878 lines
39 KiB
Nim
## Virtual bullet tracker.
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## Spawns virtual bullets per gun×power bin every tick (no real firing).
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## Resolves by travel distance. Rolling window fitness per gun×power.
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## Calls onResult() on the owning gun when a bullet resolves.
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import std/math
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import std/tables
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import std/random
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import std/algorithm
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import std/os
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import std/strutils
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import gun_interface
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const
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PowerBins* = [1.0, 1.5, 2.0, 3.0] ## 4 bins; ponytail: fixed array, add runtime config if needed
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WindowSize* = 100 ## rolling window ticks for fitness
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MaxBullets* = 8192 ## hard cap; ring buffer. 52 spawns/tick and a
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## full-map long shot (~90 ticks) need ~4700 slots;
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## 8192 wraps only after ~157 ticks. Each VirtualBullet
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## is ~120 bytes, so this array costs ~960 KiB.
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MinHitRate* = 0.40 ## LEGACY absolute bar; no longer used by bestPower
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## (no bin on the live path-metric scale cleared it, so
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## once every bin had data bestPower fell to power 1.0).
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PowerBarFrac* = 0.50 ## RELATIVE power bar (dimensionless): a bin is
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## acceptable when its virtual hit rate is at least this
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## FRACTION of the same gun's best bin rate. Scales with
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## the metric instead of assuming a ~40% hit rate.
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MinObsBeforeCompete* = 50 ## min observations before a gun×bin enters competition
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TieMargin* = 0.02 ## ABSOLUTE mode: guns within this hit-rate margin of best are tied
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MinHitRateFloor* = 0.10 ## ABSOLUTE mode: if best gun < this, fall back to gun 0 (HeadOn)
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RelTieMargin* = 0.20 ## RELATIVE mode: tied if rate >= bestRate*(1-this). Dimensionless
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## fraction of the best rate, so it scales with the metric.
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FloorPeakFrac* = 0.25 ## RELATIVE mode: floor fires if bestRate < this*peakRateRef.
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## Dimensionless: only if the field collapsed vs its own recent best.
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SelectorWindow* = 256 ## ticks of per-tick bestRate kept for the RELATIVE floor reference
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# ── selector hysteresis (anti-chatter) ─────────────────────────────────────
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#
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# Without these, `chooseFromFit` performs a fresh uniform draw among the tied
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# set EVERY tick. With the 20% relative tie band that is most of the rack, so
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# the turret re-aims on ~37% of ticks (live config log; offline replay of the
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# same selection path: 54.3 switches / 100 ticks). The two constants below
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# make the selector commit.
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#
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# Shipped values are the CONSERVATIVE end of a measured sweep against the real
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# DrussGT (8-16 runs x 7 rounds per setting, server-side events sidecar).
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# Offline replay of the same selection path gives the chatter column:
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# dwell 0 / margin 0.00 : chatter 54.3/100t real 7.02% 217 dmg/run
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# dwell 10 / margin 0.05 : chatter 1.95/100t real 6.22% 191 dmg/run
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# dwell 30 / margin 0.15 : chatter 1.33/100t real 5.10% 156 dmg/run
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# dwell 60 / margin 0.30 : real 5.72% 175 dmg/run (8 runs)
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# The light setting cuts chatter ~28x and is the only one not distinguishable
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# from the no-hysteresis baseline (overlapping per-run ranges, permutation
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# p=0.18); the heavier settings cost real hit rate (moderate p=0.002). So the
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# conservative setting ships. Set BOTH env knobs to 0 to disable hysteresis.
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GunDwellTicks* = 10 ## TICKS: once selected, a gun is held for at least
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## this many ticks unless it becomes disqualified
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## (below the eligibility sample gate) or the field
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## collapses (existing floor path -> HeadOn).
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GunSwitchMargin* = 0.05 ## DIMENSIONLESS fraction of the incumbent's score: a
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## challenger must beat the incumbent by more than
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## this (score > incumbent * (1 + margin)) before it is
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## allowed to displace it. A merely-tied gun cannot.
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MetricEnvVar* = "GUN_VBULLET_METRIC"
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## RUNTIME switch selecting how a virtual bullet is scored. Read once per
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## process at module init, so the SAME compiled binary can be A/B'd by
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## exporting it — no rebuild needed. Both the live ModularBot tracker and
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## the offline range replay call `initTracker`, so they always agree.
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type
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GunId* = int ## index into the guns seq
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BulletMetric* = enum
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bmPoint ## A bullet is scored at the single point it reaches at the
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## fire-time aim distance. HIT iff that point is within BotRadius of
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## the target on that tick. Measures prediction accuracy (does the
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## bullet arrive at the predicted point at the right time).
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bmPath ## DEFAULT. The bullet flies along its straight ray until it leaves
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## the arena. Each tick the swept segment (previous -> new position)
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## is tested against the target's radius; HIT iff ANY segment came
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## within BotRadius. Measures hypothetical hit chance against the
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## target's real path. Chosen by the DrussGT A/B: 7.2-7.6% real hit
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## rate vs 3.8% for point (p<0.0001).
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const DefaultMetric* = bmPath
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## Shipped virtual-bullet scoring model. `GUN_VBULLET_METRIC` overrides it at
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## runtime; an unset OR empty value means this default.
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proc parseMetric*(value: string): BulletMetric =
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## Parse a `GUN_VBULLET_METRIC` value. Empty / unknown values fall back to
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## the shipped `DefaultMetric` and emit a one-line warning on stderr, so a
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## typo can never silently change the metric and a bad value can never take
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## the bot down.
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case value.strip().toLowerAscii()
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of "", "default": DefaultMetric
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of "point", "points", "bmpoint": bmPoint
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of "path", "paths", "bmpath": bmPath
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else:
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stderr.writeLine("[gun_harness] unknown " & MetricEnvVar & "='" & value &
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"'; falling back to '" & $DefaultMetric & "' (valid: point|path)")
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DefaultMetric
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let ActiveMetric* = parseMetric(getEnv(MetricEnvVar, ""))
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## The metric every tracker uses unless a caller overrides it explicitly in
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## `initTracker`. Frozen at process start from the environment.
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const SelectorModeEnvVar* = "GUN_SELECTOR_MODE"
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## RUNTIME switch selecting the selection-threshold model. Read once per
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## process, so one binary can A/B both (§ virtual_bullets).
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type
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SelectorMode* = enum
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smAbsolute ## legacy: fixed 2pp tie band + 10% absolute floor. Correct only
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## if the virtual hit-rate scale happens to land near 10%.
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smRelative ## scale-aware: tie band is a fraction of the best rate; the floor
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## fires only when the field has collapsed vs its own recent peak.
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proc parseSelectorMode*(value: string): SelectorMode =
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## Empty / unknown values fall back to the shipped `relative` model and warn.
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case value.strip().toLowerAscii()
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of "", "relative", "rel": smRelative
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of "absolute", "abs", "legacy": smAbsolute
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else:
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stderr.writeLine("[gun_harness] unknown " & SelectorModeEnvVar & "='" & value &
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"'; falling back to 'relative' (valid: absolute|relative)")
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smRelative
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let ActiveSelectorMode* = parseSelectorMode(getEnv(SelectorModeEnvVar, "relative"))
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# ── runtime ranking knobs (A/B without rebuilding) ────────────────────────────
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#
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# Every knob below defaults to the SHIPPED constant, so an unset environment
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# reproduces the shipped behaviour byte-for-byte. They exist so one compiled
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# binary can be swept across candidate ranking rules. The measured A/B found no
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# candidate that credibly beats the shipped statistic: keep the defaults below
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# unless a new adversary/run set changes that.
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type
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RankStat* = enum
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rsMean ## plain window mean (SHIPPED)
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rsWilson ## Wilson lower confidence bound (z=1); penalises small n
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rsUCB ## mean + c*se (exploration bonus)
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rsThompson ## one Normal-approx Beta(h+1,n-h+1) sample per gun (Thompson)
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rsShrunk ## empirical-Bayes shrink toward the field mean
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proc envInt(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())
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except ValueError: default
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proc envFloat(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())
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except ValueError: default
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proc envBool(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 parseRank(value: string): RankStat =
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case value.strip().toLowerAscii()
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of "", "mean", "avg": rsMean
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of "wilson", "lcb": rsWilson
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of "ucb": rsUCB
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of "thompson", "ts": rsThompson
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of "shrunk", "shrink", "eb": rsShrunk
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else:
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stderr.writeLine("[gun_harness] unknown GUN_SELECTOR_RANK='" & value &
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"'; falling back to 'mean' (valid: mean|wilson|ucb|thompson|shrunk)")
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rsMean
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let ActiveWindow* = clamp(envInt("GUN_SELECTOR_WINDOW", WindowSize), 1, WindowSize)
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let ActiveMinObs* = max(1, envInt("GUN_SELECTOR_MINOBS", MinObsBeforeCompete))
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let ActiveRelTie* = envFloat("GUN_SELECTOR_TIE", RelTieMargin)
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let ActiveFloorFrac* = envFloat("GUN_SELECTOR_FLOOR", FloorPeakFrac)
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let ActivePooled* = envBool("GUN_SELECTOR_POOL", true)
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let ActiveRank* = parseRank(getEnv("GUN_SELECTOR_RANK", ""))
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let ActiveShrink* = max(0.0, envFloat("GUN_SELECTOR_SHRINK", 20.0))
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# Runtime hysteresis knobs, read once per process like the rest, so one binary
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# can be swept. Defaults equal the shipped constants; setting BOTH to 0
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# reproduces the pre-hysteresis (pure per-tick) selection for A/B.
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let ActiveDwellTicks* = max(0, envInt("GUN_SELECTOR_DWELL", GunDwellTicks))
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let ActiveSwitchMargin* = max(0.0, envFloat("GUN_SELECTOR_MARGIN", GunSwitchMargin))
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# Optional per-process seed so independent A/B runs use independent tie-breaks
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# (Nim's default rand() stream is identical in every process, which would make
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# "random" tie-breaks repeat across runs). Unset => leave the RNG untouched.
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block:
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let s = getEnv("GUN_SELECTOR_SEED", "")
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if s.len > 0:
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try: randomize(parseInt(s.strip()))
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except ValueError: discard
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type
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VirtualBullet* = object
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gunId*: GunId
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powerBin*: int ## index into PowerBins
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targetId*: int ## enemy bot ID this bullet was aimed at
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fireTick*: int ## tick this bullet was spawned; lets a gun pair its
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## predict() trace with the exact resolution event
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fireX*, fireY*: float
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aimX*, aimY*: float ## predicted target (absolute)
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bulletSpeed*: float
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travelDist*: float ## accumulated px so far
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fireDist*: float ## distance to target at fire time
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active*: bool
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# --- path-metric bookkeeping (unused by the point metric) ---
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hitSeen*: bool ## a swept segment already touched the target
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bestMissDist*: float ## closest segment->target distance seen so far
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bestMissX*: float ## target position at that closest approach
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bestMissY*: float
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FitnessWindow* = object
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## Ring buffer of hit booleans.
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hits*: array[WindowSize, bool]
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count*: int ## total samples so far (capped at WindowSize for rate)
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head*: int
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GunFitness* = object
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bins*: array[len(PowerBins), FitnessWindow]
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SelectorDiag* = object
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## Optional observability for `chooseFromFit`/`bestGun`. Never needed by the
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## bot; lets the offline range report WHY a gun was selected (floor vs tie).
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bestRate*: float ## max hit rate over eligible guns (the floor comparison value)
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floorFired*: bool ## bestRate below the active floor -> returned gun 0
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tiedCount*: int ## eligible guns within the tie band of bestRate (0 if floor fired)
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anyQualifies*: bool ## at least one gun reached MinObsBeforeCompete
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floorRate*: float ## the floor actually applied this tick
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incumbentKept*: bool ## hysteresis retained the incumbent this tick
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VirtualTracker* = object
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bullets*: array[MaxBullets, VirtualBullet]
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head*: int ## ring buffer head
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numGuns*: int
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metric*: BulletMetric ## scoring model (defaults to ActiveMetric)
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fitness*: Table[int, seq[GunFitness]] ## keyed by enemy bot ID, indexed by GunId
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droppedBullets*: int ## unresolved bullets clobbered by the ring buffer (should stay 0)
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# RELATIVE-mode floor reference: per-tick bestRate history + its running max.
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rateHist*: array[SelectorWindow, float]
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rateHistHead*: int
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rateHistCount*: int
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peakRateRef*: float ## max bestRate in rateHist; 0.0 = not enough history yet
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# Hysteresis state (see `selectGun`): the incumbent gun and the tick it was
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# chosen on. `-1` means no gun selected yet. Only the live `selectGun` path
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# reads/writes these; `bestGun`/`chooseFromFit` stay memoryless.
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currentGun*: GunId
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currentSince*: int
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proc initTracker*(numGuns: int, metric = ActiveMetric): VirtualTracker =
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## `metric` defaults to the process-wide `GUN_VBULLET_METRIC` switch; pass it
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## explicitly only from tests that need both models in one process.
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result.numGuns = numGuns
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result.metric = metric
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result.currentGun = -1
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proc windowHits(fw: FitnessWindow, want: int): int =
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## Hits among the most recent `want` samples, in ring order. Reading the last
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## `want` slots (head backwards) is what makes a runtime window shorter than
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## `WindowSize` correct even after the ring has wrapped.
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let n = min(fw.count, min(want, WindowSize))
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for k in 1..n:
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let idx = (fw.head - k + WindowSize) mod WindowSize
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if fw.hits[idx]: inc result
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proc hitRate*(fw: FitnessWindow): float =
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## Returns fraction of hits in the rolling window. 0.0 when no data.
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## Uses `ActiveWindow` (defaults to `WindowSize`).
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if fw.count == 0: return 0.0
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let n = min(fw.count, min(ActiveWindow, WindowSize))
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if n == 0: return 0.0
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result = windowHits(fw, n).float / n.float
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proc record(fw: var FitnessWindow, hit: bool) =
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fw.hits[fw.head] = hit
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fw.head = (fw.head + 1) mod WindowSize
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inc fw.count
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proc spawnBullets*(t: var VirtualTracker, gunId: GunId,
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predictions: array[len(PowerBins), GunPrediction],
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state: WorldState, targetId: int) =
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## Call once per gun per tick with predictions for all power bins.
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## Lazily creates fitness entry for targetId on first spawn.
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if targetId notin t.fitness:
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t.fitness[targetId] = newSeq[GunFitness](t.numGuns)
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for binIdx in 0..<len(PowerBins):
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let power = PowerBins[binIdx]
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let speed = bulletSpeed(power)
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let pred = predictions[binIdx]
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let fireDist = hypot(pred.x - state.selfX, pred.y - state.selfY)
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let slot = t.head mod MaxBullets
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# Measurement integrity: if the slot we are about to overwrite still holds an
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# unresolved bullet, that bullet will never be scored. Count it instead of
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# silently dropping it (non-zero after a battle means MaxBullets is too small).
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if t.bullets[slot].active:
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inc t.droppedBullets
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t.bullets[slot] = VirtualBullet(
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gunId: gunId,
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powerBin: binIdx,
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targetId: targetId,
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fireTick: state.tick,
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fireX: state.selfX,
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fireY: state.selfY,
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aimX: pred.x,
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aimY: pred.y,
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bulletSpeed: speed,
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travelDist: 0.0,
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fireDist: fireDist,
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active: true,
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hitSeen: false,
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bestMissDist: Inf,
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bestMissX: 0.0,
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bestMissY: 0.0,
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)
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t.head = (t.head + 1) mod MaxBullets
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const StaleTicks* = 20 ## discard bullet if target not seen within this many ticks
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proc distPointToSegment*(px, py, ax, ay, bx, by: float): float =
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## Shortest distance from point P to the segment A-B (A/B are bullet
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## positions on consecutive ticks).
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let abx = bx - ax
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let aby = by - ay
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let abLen2 = abx*abx + aby*aby
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var s = 0.0
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if abLen2 > 1e-12:
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s = clamp(((px - ax)*abx + (py - ay)*aby) / abLen2, 0.0, 1.0)
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hypot(px - (ax + s*abx), py - (ay + s*aby))
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# ── rate helpers (shared by the selector and the reference tracker) ──────────
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proc gunEligible*(fit: GunFitness, requireMin: bool): bool =
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## True if a gun has >= MinObsBeforeCompete samples in at least one bin (or
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## when `requireMin` is false, every gun is eligible).
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if not requireMin: return true
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for binIdx in 0..<len(PowerBins):
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if fit.bins[binIdx].count >= ActiveMinObs: return true
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false
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proc gunCounts*(fit: GunFitness, pooled: bool): tuple[hits, n: int] =
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## Sample counts behind `gunRate`. `pooled` sums all power bins; otherwise the
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## single bin with the best rate (the "specialist" view).
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if pooled:
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for binIdx in 0..<len(PowerBins):
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let m = min(fit.bins[binIdx].count, min(ActiveWindow, WindowSize))
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result.n += m
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result.hits += windowHits(fit.bins[binIdx], m)
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else:
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var best = -1.0
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for binIdx in 0..<len(PowerBins):
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let m = min(fit.bins[binIdx].count, min(ActiveWindow, WindowSize))
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if m == 0: continue
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let h = windowHits(fit.bins[binIdx], m)
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let r = h.float / m.float
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if r > best:
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best = r
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result = (h, m)
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proc gunRate*(fit: GunFitness, pooled: bool): float =
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## A gun's hit rate. `pooled` sums hits/shots across all power bins (more
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## samples, immune to one lucky bin); otherwise the max single-bin rate.
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let (h, n) = gunCounts(fit, pooled)
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result = if n > 0: h.float / n.float else: 0.0
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proc rankScore(fit: GunFitness, pooled: bool, stat: RankStat,
|
||
fieldRate, shrinkK: float): float =
|
||
## Ranking statistic over the gun's window. All are monotone-ish in the mean,
|
||
## but differ in how they trade mean against sample size/noise. `rsMean` is the
|
||
## shipped statistic.
|
||
let (h, n) = gunCounts(fit, pooled)
|
||
if n == 0: return 0.0
|
||
let p = h.float / n.float
|
||
case stat
|
||
of rsMean:
|
||
p
|
||
of rsWilson:
|
||
let z = 1.0
|
||
let z2 = z * z
|
||
let denom = 1.0 + z2 / n.float
|
||
let centre = p + z2 / (2.0 * n.float)
|
||
let margin = z * sqrt((p * (1.0 - p) + z2 / (4.0 * n.float)) / n.float)
|
||
max(0.0, (centre - margin) / denom)
|
||
of rsUCB:
|
||
p + 0.5 * sqrt(p * (1.0 - p) / n.float)
|
||
of rsThompson:
|
||
let se = sqrt(max(1e-9, p * (1.0 - p) / n.float))
|
||
clamp(p + gauss(0.0, se), 0.0, 1.0)
|
||
of rsShrunk:
|
||
(h.float + shrinkK * fieldRate) / (n.float + shrinkK)
|
||
|
||
proc tableBestRate(t: VirtualTracker, pooled: bool): float =
|
||
## Best eligible gun rate across every target's fitness (no merge/allocation).
|
||
## Only guns with >= MinObsBeforeCompete samples count: under-sampled bins
|
||
## produce 100%/-looking spikes that would inflate the floor reference and
|
||
## force HeadOn for the whole window. Zero when nothing is warmed up yet.
|
||
result = 0.0
|
||
for _, perEnemy in t.fitness:
|
||
for gunId in 0..<perEnemy.len:
|
||
if not gunEligible(perEnemy[gunId], true): continue
|
||
result = max(result, gunRate(perEnemy[gunId], pooled))
|
||
|
||
proc noteBestRate*(t: var VirtualTracker) =
|
||
## Record this tick's field-best rate and refresh the RELATIVE-mode floor
|
||
## reference: the max bestRate over the last SelectorWindow ticks. Call once
|
||
## per selection tick; the live loop does this from `tickBullets`.
|
||
let best = tableBestRate(t, pooled = true)
|
||
t.rateHist[t.rateHistHead] = best
|
||
t.rateHistHead = (t.rateHistHead + 1) mod SelectorWindow
|
||
if t.rateHistCount < SelectorWindow: inc t.rateHistCount
|
||
var peak = 0.0
|
||
for i in 0..<t.rateHistCount:
|
||
if t.rateHist[i] > peak: peak = t.rateHist[i]
|
||
t.peakRateRef = peak
|
||
|
||
proc tickBullets*(t: var VirtualTracker, state: WorldState,
|
||
enemies: Table[int, tuple[x, y: float, lastSeenTick: int, alive: bool]],
|
||
onResolved: proc(gunId: GunId, binIdx: int, e: FeedbackEvent)) =
|
||
## Advance all active bullets one tick.
|
||
##
|
||
## The `bmPoint` branch (default) is unchanged: resolve when the bullet
|
||
## reaches the fire-time aim distance and score the single point it lands on.
|
||
##
|
||
## The `bmPath` branch flies the bullet along its ray until it leaves the
|
||
## arena and tests each tick's swept segment against the target's radius. It
|
||
## records exactly one outcome per bullet (at the wall), so every resolved
|
||
## bullet contributes exactly one fitness sample. A bullet that goes dead or
|
||
## stale is discarded without scoring, mirroring the point metric at
|
||
## resolution time.
|
||
for i in 0..<MaxBullets:
|
||
var b = addr t.bullets[i]
|
||
if not b.active: continue
|
||
b.travelDist += b.bulletSpeed
|
||
|
||
case t.metric
|
||
of bmPoint:
|
||
if b.travelDist < b.fireDist: continue
|
||
|
||
# Resolved: look up the correct enemy position
|
||
var ex, ey: float
|
||
if b.targetId in enemies:
|
||
let e = enemies[b.targetId]
|
||
if not e.alive or (state.tick - e.lastSeenTick) > StaleTicks:
|
||
b.active = false
|
||
continue
|
||
ex = e.x; ey = e.y
|
||
else:
|
||
# No data for this target — fall back to selected enemy in state
|
||
ex = state.enemyX; ey = state.enemyY
|
||
|
||
let dx = b.aimX - b.fireX
|
||
let dy = b.aimY - b.fireY
|
||
let dist = hypot(dx, dy)
|
||
let (bx, by) =
|
||
if dist < 1e-6: (b.aimX, b.aimY)
|
||
else: (b.fireX + dx / dist * b.travelDist,
|
||
b.fireY + dy / dist * b.travelDist)
|
||
let missDist = hypot(bx - ex, by - ey)
|
||
let hit = missDist < BotRadius
|
||
|
||
if b.targetId in t.fitness:
|
||
t.fitness[b.targetId][b.gunId].bins[b.powerBin].record(hit)
|
||
|
||
let fe = FeedbackEvent(
|
||
prediction: GunPrediction(x: b.aimX, y: b.aimY),
|
||
actualX: ex,
|
||
actualY: ey,
|
||
bulletPower: PowerBins[b.powerBin],
|
||
fireTick: b.fireTick,
|
||
powerBin: b.powerBin,
|
||
missDistance: missDist,
|
||
hit: hit,
|
||
)
|
||
onResolved(b.gunId, b.powerBin, fe)
|
||
b.active = false
|
||
|
||
of bmPath:
|
||
# Enemy pose for this tick. A dead/stale target abandons the bullet
|
||
# without scoring, exactly as the point metric does at resolution time.
|
||
var ex, ey: float
|
||
if b.targetId in enemies:
|
||
let e = enemies[b.targetId]
|
||
if not e.alive or (state.tick - e.lastSeenTick) > StaleTicks:
|
||
b.active = false
|
||
continue
|
||
ex = e.x; ey = e.y
|
||
else:
|
||
ex = state.enemyX; ey = state.enemyY
|
||
|
||
let dx = b.aimX - b.fireX
|
||
let dy = b.aimY - b.fireY
|
||
let dist = hypot(dx, dy)
|
||
var ux, uy: float
|
||
if dist < 1e-6: ux = 0.0; uy = 0.0
|
||
else: ux = dx / dist; uy = dy / dist
|
||
let prevD = max(0.0, b.travelDist - b.bulletSpeed)
|
||
let ax = b.fireX + ux * prevD
|
||
let ay = b.fireY + uy * prevD
|
||
let bx = b.fireX + ux * b.travelDist
|
||
let by = b.fireY + uy * b.travelDist
|
||
let segMiss = distPointToSegment(ex, ey, ax, ay, bx, by)
|
||
|
||
if not b.hitSeen:
|
||
if segMiss < BotRadius:
|
||
# First physical contact — freeze it so a later closer approach
|
||
# cannot overwrite the contact position the guns learn from.
|
||
b.hitSeen = true
|
||
b.bestMissDist = segMiss
|
||
b.bestMissX = ex
|
||
b.bestMissY = ey
|
||
elif segMiss < b.bestMissDist:
|
||
b.bestMissDist = segMiss
|
||
b.bestMissX = ex
|
||
b.bestMissY = ey
|
||
|
||
# Despawn only at a wall (a degenerate zero-length ray also ends here).
|
||
let outside =
|
||
dist < 1e-6 or
|
||
bx < 0.0 or bx > state.arenaWidth or
|
||
by < 0.0 or by > state.arenaHeight
|
||
if outside:
|
||
let missDist = if b.bestMissDist == Inf: segMiss else: b.bestMissDist
|
||
let rx = if b.bestMissDist == Inf: ex else: b.bestMissX
|
||
let ry = if b.bestMissDist == Inf: ey else: b.bestMissY
|
||
if b.targetId in t.fitness:
|
||
t.fitness[b.targetId][b.gunId].bins[b.powerBin].record(b.hitSeen)
|
||
let fe = FeedbackEvent(
|
||
prediction: GunPrediction(x: b.aimX, y: b.aimY),
|
||
actualX: rx,
|
||
actualY: ry,
|
||
bulletPower: PowerBins[b.powerBin],
|
||
fireTick: b.fireTick,
|
||
powerBin: b.powerBin,
|
||
missDistance: missDist,
|
||
hit: b.hitSeen,
|
||
)
|
||
onResolved(b.gunId, b.powerBin, fe)
|
||
b.active = false
|
||
|
||
if ActiveSelectorMode == smRelative:
|
||
noteBestRate(t)
|
||
|
||
proc fitnessFor*(t: VirtualTracker, targetId: int): seq[GunFitness] =
|
||
## Returns fitness seq for targetId, or merges all enemies as fallback.
|
||
##
|
||
## The fallback is a RECENCY-WEIGHTED AGGREGATE over the last WindowSize
|
||
## samples, NOT a pooled rate: each per-enemy window is replayed into one fresh
|
||
## window, so once the total exceeds WindowSize the earliest samples are
|
||
## overwritten by later ones. Enemies are visited in ascending target-id order
|
||
## so the result is identical on every run (std/tables iteration order is hash
|
||
## order and therefore nondeterministic).
|
||
## ponytail: merge is O(enemies*guns*bins*WindowSize), fine for small counts
|
||
if targetId >= 0 and targetId in t.fitness:
|
||
return t.fitness[targetId]
|
||
# Aggregate across all enemies, deterministically ordered.
|
||
result = newSeq[GunFitness](t.numGuns)
|
||
var enemyIds: seq[int]
|
||
for id in t.fitness.keys: enemyIds.add id
|
||
enemyIds.sort()
|
||
for id in enemyIds:
|
||
let perEnemy = t.fitness[id]
|
||
for gunId in 0..<t.numGuns:
|
||
for binIdx in 0..<len(PowerBins):
|
||
let src = perEnemy[gunId].bins[binIdx]
|
||
for k in 0..<min(src.count, WindowSize):
|
||
result[gunId].bins[binIdx].record(src.hits[k])
|
||
|
||
proc bestPower*(t: VirtualTracker, gunId: GunId, targetId: int = -1): (int, float) =
|
||
## Returns (binIdx, power). Prefers the HIGHEST power bin whose virtual hit
|
||
## rate is acceptable, where "acceptable" is measured RELATIVE to the same
|
||
## gun's best bin (`rate >= PowerBarFrac * bestBinRate`, dimensionless) — not
|
||
## against the legacy absolute `MinHitRate`. On the live path-metric scale a
|
||
## gun's rates sit around 3-40%, so the absolute 40% bar never fired once every
|
||
## bin had data and bestPower silently collapsed to power 1.0; the relative bar
|
||
## discriminates between bins at any scale.
|
||
##
|
||
## An EMPTY bin is still handed out (highest power first) so every bin keeps
|
||
## getting sampled, and a fully cold gun (no data anywhere) returns the lowest
|
||
## power bin. Uses per-enemy fitness when targetId >= 0 and data exists; else
|
||
## the deterministic aggregate.
|
||
let fit = t.fitnessFor(targetId)
|
||
result = (0, PowerBins[0])
|
||
var anyObs = false
|
||
var bestRate = 0.0
|
||
for binIdx in 0..<len(PowerBins):
|
||
if fit[gunId].bins[binIdx].count > 0:
|
||
anyObs = true
|
||
bestRate = max(bestRate, fit[gunId].bins[binIdx].hitRate())
|
||
if not anyObs:
|
||
return (0, PowerBins[0])
|
||
let bar = PowerBarFrac * bestRate
|
||
for binIdx in countdown(len(PowerBins) - 1, 0):
|
||
let fw = fit[gunId].bins[binIdx]
|
||
if fw.count == 0 or fw.hitRate() >= bar:
|
||
return (binIdx, PowerBins[binIdx])
|
||
|
||
# ── energy-aware power policy (TR_POWER_*) ───────────────────────────────────
|
||
#
|
||
# `bestPower` answers "which power bin does this gun's own virtual data prefer?"
|
||
# and is deliberately left untouched. The policy below CAPS that preference using
|
||
# only cheap, always-available state — range, our own energy, and the gun's own
|
||
# rate — so a long-range or low-energy shot trades single-hit damage for a
|
||
# faster bullet (speed = 20-3p, so LOW power is FASTER and needs less lead) and a
|
||
# shorter fire interval (10+2p, so LOW power = MORE shots). It never RAISES
|
||
# power, so the shipped behaviour is exactly the `cap = 3.0` case, which is also
|
||
# the control arm (`TR_POWER_POLICY=0`).
|
||
#
|
||
# Measured basis (real shots vs DrussGT, 8-16 runs): hit rate 21.6% at 0-100px,
|
||
# 27.1% at 100-200, then 19.3% at 200-300, 10.9% at 300-400, 6.8% at 400-600 and
|
||
# 5.4% at 600-800. Energy math: E[dE] = p(3P-1), so the break-even hit
|
||
# probability is 1/3 INDEPENDENT of power; at range/low energy the extra speed
|
||
# and shots of p=1.0 dominate. Damage is 4p (p<=1) / 6p-2 (p>1).
|
||
const
|
||
PowerPolicyEnvVar* = "TR_POWER_POLICY" ## 0 = control arm (uncapped)
|
||
PowerFarDistEnvVar* = "TR_POWER_FAR_DIST" ## px; beyond this = bad-chances zone
|
||
PowerLowEnergyEnvVar* = "TR_POWER_LOW_ENERGY" ## self energy below this = conserve
|
||
PowerFarCapEnvVar* = "TR_POWER_FAR_CAP" ## cap for far / low-energy
|
||
PowerMidCapEnvVar* = "TR_POWER_MID_CAP" ## cap when close+healthy but not above avg
|
||
PowerRefEnvVar* = "TR_POWER_REF" ## 0 = gun's own mean; >0 = fixed P_ref
|
||
|
||
let PowerPolicyEnabled* = envBool(PowerPolicyEnvVar, true)
|
||
let PowerFarDist* = envFloat(PowerFarDistEnvVar, 200.0)
|
||
let PowerLowEnergy* = envFloat(PowerLowEnergyEnvVar, 50.0)
|
||
let PowerFarCap* = envFloat(PowerFarCapEnvVar, 1.0)
|
||
let PowerMidCap* = envFloat(PowerMidCapEnvVar, 2.0)
|
||
let PowerRefFixed* = envFloat(PowerRefEnvVar, 0.0)
|
||
|
||
type
|
||
PowerReason* = enum
|
||
prFull ## above-average chances, close, healthy -> full power
|
||
prFar ## beyond TR_POWER_FAR_DIST -> bad-chances zone
|
||
prLowEnergy ## self energy below TR_POWER_LOW_ENERGY -> conserve
|
||
prBelowAvg ## chances not above the gun's own average -> no power 3.0
|
||
prRam ## ramming: exempt (at contact P->1, so 3.0 is correct)
|
||
|
||
PowerCap* = object
|
||
power*: float ## the power to fire (<= the gun's preference)
|
||
cap*: float ## the cap applied (3.0 = uncapped)
|
||
reason*: PowerReason ## why
|
||
|
||
proc powerReasonName*(r: PowerReason): string =
|
||
case r
|
||
of prFull: "full"
|
||
of prFar: "far"
|
||
of prLowEnergy: "lowEnergy"
|
||
of prBelowAvg: "belowAvg"
|
||
of prRam: "ram"
|
||
|
||
proc binIndexForPower*(power: float): int =
|
||
## Index of `power` in `PowerBins`; if it is not an exact bin value, the
|
||
## highest bin whose power does not exceed it (0 if none). Keeps the returned
|
||
## bin index consistent with a capped power.
|
||
result = 0
|
||
for i in 0..<len(PowerBins):
|
||
if abs(PowerBins[i] - power) < 1e-9: return i
|
||
if PowerBins[i] <= power: result = i
|
||
|
||
proc applyPowerPolicy*(preferredPower, dist, selfEnergy, pEst, pRef: float,
|
||
ramming: bool,
|
||
enabled = PowerPolicyEnabled,
|
||
farDist = PowerFarDist,
|
||
lowEnergy = PowerLowEnergy,
|
||
farCap = PowerFarCap,
|
||
midCap = PowerMidCap): PowerCap =
|
||
## PURE cap core — no tracker, no battle. `power = min(preferredPower, cap)`,
|
||
## so the result can only ever LOWER the gun's own preference. Order of
|
||
## precedence: ram (exempt) > far > low energy > below average > full.
|
||
##
|
||
## `pEst` is the gun's rate for the bin it chose (or its aggregate when that
|
||
## bin is empty); `pRef` is the gun's aggregate mean (or the fixed
|
||
## `TR_POWER_REF`). A cold gun has no data, so `pEst <= pRef` is vacuously
|
||
## true and it gets the mid cap — deliberately conservative until it has
|
||
## evidence its chances are above average.
|
||
if ramming:
|
||
return PowerCap(power: preferredPower, cap: 3.0, reason: prRam)
|
||
if not enabled:
|
||
return PowerCap(power: preferredPower, cap: 3.0, reason: prFull)
|
||
var cap: float
|
||
var reason: PowerReason
|
||
if dist > farDist:
|
||
cap = farCap
|
||
reason = prFar
|
||
elif selfEnergy < lowEnergy:
|
||
cap = farCap
|
||
reason = prLowEnergy
|
||
elif pEst <= pRef:
|
||
cap = midCap
|
||
reason = prBelowAvg
|
||
else:
|
||
cap = 3.0
|
||
reason = prFull
|
||
PowerCap(power: min(preferredPower, cap), cap: cap, reason: reason)
|
||
|
||
proc chooseFromFit*(fit: seq[GunFitness], diag: ptr SelectorDiag = nil,
|
||
mode: SelectorMode = smAbsolute,
|
||
referenceRate = -1.0,
|
||
incumbent: GunId = -1,
|
||
switchMargin = 0.0): GunId =
|
||
## Core gun ranking over an already-resolved fitness seq. Split out from
|
||
## `bestGun` so the offline range can rank without copying a VirtualTracker,
|
||
## and so callers can request `diag` for the selection internals.
|
||
##
|
||
## Guns with fewer than MinObsBeforeCompete observations are skipped unless
|
||
## every gun is below threshold (then fall back to best of all).
|
||
##
|
||
## `mode` chooses the threshold model:
|
||
## smAbsolute — legacy fixed TieMargin / MinHitRateFloor.
|
||
## smRelative — tie band = bestRate*RelTieMargin; floor = FloorPeakFrac
|
||
## * `referenceRate` (the recent field-best rate). Pooled over
|
||
## power bins, since one lucky bin is a poor ranker.
|
||
## `referenceRate` <= 0 disables the RELATIVE floor (no history yet).
|
||
##
|
||
## `incumbent` (>= 0) enables switch hysteresis: the incumbent is retained
|
||
## unless the best eligible gun beats it by the RELATIVE `switchMargin`
|
||
## (score > incumbentScore * (1 + margin)). Defaults keep the pure-ranking
|
||
## behaviour the offline analyzer and the `bestGun` tests rely on.
|
||
##
|
||
## Ties (within the band) are broken randomly to avoid index-0 bias. The draw
|
||
## runs ONLY when a switch is actually permitted — a tick that retains the
|
||
## incumbent returns before `rand`, so the tie-break no longer re-decides
|
||
## every tick.
|
||
let pooled = if mode == smRelative: ActivePooled else: false
|
||
|
||
var anyQualifies = false
|
||
for gunId in 0..<fit.len:
|
||
if gunEligible(fit[gunId], true):
|
||
anyQualifies = true
|
||
break
|
||
let requireMin = anyQualifies
|
||
if diag != nil: diag[].anyQualifies = requireMin
|
||
|
||
# Mean rate per eligible gun: used both for the floor reference and (for
|
||
# rsShrunk) as the field mean the estimates are pulled toward.
|
||
var bestRate = 0.0
|
||
var fieldSum = 0.0
|
||
var fieldN = 0
|
||
for gunId in 0..<fit.len:
|
||
if requireMin and not gunEligible(fit[gunId], true): continue
|
||
let (h, n) = gunCounts(fit[gunId], pooled)
|
||
if n == 0: continue
|
||
let r = h.float / n.float
|
||
bestRate = max(bestRate, r)
|
||
fieldSum += r
|
||
inc fieldN
|
||
let fieldRate = if fieldN > 0: fieldSum / fieldN.float else: 0.0
|
||
if diag != nil: diag[].bestRate = bestRate
|
||
|
||
let floorRate =
|
||
if mode == smAbsolute: MinHitRateFloor
|
||
elif referenceRate > 0.0: ActiveFloorFrac * referenceRate
|
||
else: 0.0
|
||
if diag != nil: diag[].floorRate = floorRate
|
||
|
||
# No hit at all, or the field collapsed below its own recent peak: HeadOn.
|
||
if bestRate <= 0.0 or bestRate < floorRate:
|
||
if diag != nil: diag[].floorFired = true
|
||
return 0
|
||
|
||
# Ranking scores (the active statistic) and the best of them.
|
||
var scores = newSeq[float](fit.len)
|
||
var bestScore = 0.0
|
||
for gunId in 0..<fit.len:
|
||
if requireMin and not gunEligible(fit[gunId], true): continue
|
||
let s = rankScore(fit[gunId], pooled, ActiveRank, fieldRate, ActiveShrink)
|
||
scores[gunId] = s
|
||
bestScore = max(bestScore, s)
|
||
|
||
let tieBand =
|
||
if mode == smAbsolute: TieMargin
|
||
else: bestScore * ActiveRelTie
|
||
var tied: seq[GunId]
|
||
for gunId in 0..<fit.len:
|
||
if requireMin and not gunEligible(fit[gunId], true): continue
|
||
if scores[gunId] >= bestScore - tieBand:
|
||
tied.add(gunId)
|
||
if diag != nil: diag[].tiedCount = tied.len
|
||
|
||
# ── switch hysteresis ──────────────────────────────────────────────────────
|
||
# A settled incumbent is displaced only by a challenger that clears the
|
||
# relative margin. This is what stops a merely-tied gun from churning the
|
||
# turret. `incumbent < 0` disables the rule (pure ranking).
|
||
if incumbent >= 0 and incumbent < fit.len:
|
||
var incumbentEligible = true
|
||
if requireMin and not gunEligible(fit[incumbent], true):
|
||
incumbentEligible = false
|
||
if incumbentEligible:
|
||
let incumbentScore = scores[incumbent]
|
||
let bar = incumbentScore * (1.0 + switchMargin)
|
||
if bestScore <= bar:
|
||
if diag != nil: diag[].incumbentKept = true
|
||
return incumbent
|
||
# Keep only genuine challengers (guns that clear the margin); if none do,
|
||
# the incumbent survives even when `bestScore` technically exceeds the bar
|
||
# (the top gun may sit inside the tie band but not be switched to).
|
||
var challengers: seq[GunId]
|
||
for g in tied:
|
||
if scores[g] > bar: challengers.add g
|
||
if challengers.len > 0:
|
||
tied = challengers
|
||
else:
|
||
if diag != nil: diag[].incumbentKept = true
|
||
return incumbent
|
||
|
||
if tied.len == 0: return 0
|
||
result = tied[rand(tied.len - 1)]
|
||
|
||
proc bestGun*(t: VirtualTracker, targetId: int = -1,
|
||
diag: ptr SelectorDiag = nil): GunId =
|
||
## Pick gun with highest hit rate across all power bins.
|
||
## Uses per-enemy fitness when targetId >= 0 and data exists; else aggregate.
|
||
## `diag`, when non-nil, receives the selection internals (bestRate, floor,
|
||
## tie count) exactly as used by the decision.
|
||
##
|
||
## This is the PURE, memoryless ranking primitive: it starts a fresh tie-break
|
||
## every call. The live bot uses `selectGun` (below), which wraps it with the
|
||
## dwell/switch-margin hysteresis. Keeping this pure is what lets the offline
|
||
## analyzer and the harness tests stay deterministic and side-effect free.
|
||
result = chooseFromFit(t.fitnessFor(targetId), diag,
|
||
mode = ActiveSelectorMode,
|
||
referenceRate = t.peakRateRef)
|
||
|
||
proc selectGun*(t: var VirtualTracker, targetId: int = -1, tick = 0,
|
||
diag: ptr SelectorDiag = nil): GunId =
|
||
## Stateful, sticky gun selection — the LIVE path (`selector.selectShot` calls
|
||
## this). Wraps the pure `chooseFromFit` ranking with two commitments:
|
||
##
|
||
## * minimum dwell — once selected, a gun is held for at least
|
||
## `GUN_SELECTOR_DWELL` ticks unless it becomes disqualified (below the
|
||
## eligibility sample gate) or the field collapses (existing floor path ->
|
||
## HeadOn);
|
||
## * switch margin — a challenger must beat the incumbent by
|
||
## `GUN_SELECTOR_MARGIN` (a fraction of the incumbent's score) before it can
|
||
## displace it, so a merely-tied gun does not.
|
||
##
|
||
## Setting BOTH knobs to 0 disables hysteresis entirely and reproduces the old
|
||
## per-tick behaviour, so the same binary can A/B against the baseline.
|
||
##
|
||
## Seam: the incumbent lives in the `VirtualTracker` because the tracker
|
||
## already owns all other selection state (`fitness`, the floor's
|
||
## `peakRateRef` history) and is threaded through the whole live loop; the bot
|
||
## does not need to know about it. `bestGun`/`chooseFromFit` stay pure for the
|
||
## offline tools.
|
||
let fit = t.fitnessFor(targetId)
|
||
var local: SelectorDiag
|
||
let d = if diag != nil: diag else: addr local
|
||
|
||
# Both zero == no hysteresis: pass no incumbent, matching the pre-change
|
||
# (pure per-tick) selector for a clean A/B.
|
||
let useIncumbent = ActiveDwellTicks > 0 or ActiveSwitchMargin > 0.0
|
||
let incumbent = if useIncumbent: t.currentGun else: -1
|
||
|
||
let challenger = chooseFromFit(fit, d, ActiveSelectorMode, t.peakRateRef,
|
||
incumbent = incumbent,
|
||
switchMargin = ActiveSwitchMargin)
|
||
|
||
# The floor collapses the field to HeadOn regardless of dwell.
|
||
if d[].floorFired:
|
||
if t.currentGun != 0:
|
||
t.currentGun = 0
|
||
t.currentSince = tick
|
||
return 0
|
||
|
||
result = challenger
|
||
# Minimum dwell: hold the incumbent while it is still eligible.
|
||
if useIncumbent and t.currentGun >= 0 and t.currentGun < fit.len:
|
||
let eligible = (not d[].anyQualifies) or gunEligible(fit[t.currentGun], true)
|
||
if eligible and (tick - t.currentSince) < ActiveDwellTicks:
|
||
result = t.currentGun
|
||
|
||
if result != t.currentGun:
|
||
t.currentGun = result
|
||
t.currentSince = tick
|