b68707c867
The user's request: "when our bot is low OR enemy is low, it is useless to use high power instead low fast bullets have more chances to finish the enemy. Let's do a math slope: starting from some health down, the power goes down with it." 1. ENERGY SLOPE (`TR_POWER_ENERGY_*`), replacing the old hard step at 50 energy: cap = ENERGY_MAX at/above ENERGY_HI, ENERGY_MIN at/below ENERGY_LO, LINEAR in power between, clamped. Defaults HI=80 LO=20 MIN=0.5 MAX=3.0, so no cap >=80, 0.5 at <=20, and e.g. E=65 -> 2.375, E=50 -> 1.75, E=35 -> 1.125. Rationale: bullet speed is 20-3p, so lower power = FASTER bullet (less lead error, higher hit chance), fires more often (10+2p) and drains slower (p/shot). E[dE] = p(3P-1) => break-even hit probability is 1/3 INDEPENDENT of power, and our measured rates are 5-27%, far below it. 2. FINISHING CAP (`TR_POWER_FINISH_KILL`, default ON): cap power at the SMALLEST bullet that still removes the enemy's remaining energy - `E<=4 -> p=E/4` (min 0.1), `4<E<=16 -> p=(E+2)/6`, `E>16 -> no cap`. Rationale, and it makes the user's instinct stronger than a heuristic: server 1.3.1 caps the damage SCORE at the energy ACTUALLY REMOVED, so overkill is WASTED damage AND ~6x the energy for ZERO extra score. Damage is 4p (p<=1) / 6p-2 (p>1). Both are min-composed with the existing far/below-average caps, may only LOWER power (exhaustively tested), and are exempt while ramming. `TR_POWER_POLICY=0` still returns the uncapped control exactly. MEASURED ENERGY SAVING (offline replay of the DrussGT fixtures, 28,797 ticks): arm shots energy meanP E/1k ticks vs cliff control(uncapped) 1913 4646 2.43 161.4 -90.2% cliff (today) 2363 2443 1.03 84.8 0.0% slope 2404 2178 0.91 75.6 ** 10.9% LESS ** slope+finish 2404 2167 0.90 75.3 ** 11.3% LESS ** So the slope spends ~11% less energy than the cliff AND fires slightly MORE shots (2404 vs 2363) - both directions at once. HONEST NOTE on the finishing rule's reach here: ticks where the enemy is low (0 < E <= 16) are only 2252/28797 = 7.8% of these fixtures, so finishing adds just ~11 energy of saving against DrussGT. It matters in CLOSER fights, not this one. Verification: test_power_policy 58 (was 26) in BOTH the default and TR_POWER_POLICY=0 control arms - slope at E=100/80/65/50/35/20/5, powerToKill across E=0.1..100, the inverse-cover property for E<=16, monotonicity, ram exemption, and an exhaustive sweep proving power <= preference. Guards: test_gun_harness 39, test_vbullet_metric 11, test_power_selection 3, test_adaptive_radar 41, test_tfil_ring_weights 24, test_ram_decision 40, test_rack_membership 48, test_selector_tiebreak 19, test_tm_pattern_registration 20, test_vbullet_admit_gate 12. acceptance 12/12 PASS. ModularBot compiles release. Adds `common_libs/tests/measure_power_policy.nim` (the energy/histogram tool) and updates docs/env_reference.md for the new `energySlope|finishKill` log reasons. NOT MEASURED: the battle/hit-rate effect. The offline figures use the fixture shooter's energy as a proxy, open-loop; the RELATIVE saving is the meaningful part.
248 lines
13 KiB
Nim
248 lines
13 KiB
Nim
## Gun selector — picks best gun×power, computes aim angle, gates firing.
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## Fires highest power with acceptable hit rate when the gun is aimed within a
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## range-dependent angular tolerance and gunHeat == 0.
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import std/math
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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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import virtual_bullets
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# ── rack membership (TR_RACK_*) ──────────────────────────────────────────────
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#
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# Per-gun rack membership, read ONCE at process start so a single frozen binary
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# can be re-racked without a rebuild — the same runtime pattern as
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# GUN_RACK_DISABLE. A gun's membership admits it into the 1v1 rack, the melee
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# rack, both, or neither:
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#
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# TR_RACK_PATTERN=both (shipped default: the ONLY admitted gun)
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# TR_RACK_HEADON=both -> re-admit HeadOn (used to restore the old rack)
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# TR_RACK_TSETLIN=1v1 -> 1v1 rack only
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# TR_RACK_DISPLACE=melee -> melee rack only
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# TR_RACK_KNN=off -> removed from both racks
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# TR_RACK_TMPATTERN=off (shipped default for the new TM pattern gun)
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#
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# SHIPPED DEFAULT IS `onlyPattern`: Pattern (id 5) is admitted in both racks and
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# every other gun is `off`. This is a deliberate, measured decision, not a
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# pruning heuristic — the virtual-fitness selector was measured to be NEGATIVE
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# value at every rack size tested (full, lean8, lean6, pairPC/PK/PL) and against
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# 10/10 adversaries, while Pattern alone is the best single gun in general. See
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# docs/selector_negative_value.md. The selector MECHANISM is retained in full
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# (chooseFromFit, the floor/band logic, hysteresis, virtual fitness) — the rack
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# merely has one member by default, so re-enabling any gun is a one-line env
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# override with no rebuild:
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#
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# Revert to the old full rack (all guns `both`, TMPATTERN `off`):
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# TR_RACK_PATTERN=both TR_RACK_HEADON=both TR_RACK_LINEAR=both \
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# TR_RACK_TSETLIN=both TR_RACK_CIRCULAR=both TR_RACK_GUESSFACTOR=both \
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# TR_RACK_WALLBOUNCE=both TR_RACK_ACCEL=both TR_RACK_STOPSHOT=both \
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# TR_RACK_DISPLACE=both TR_RACK_AVGLEAD=both TR_RACK_DECAYGF=both \
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# TR_RACK_KNN=both TR_RACK_TMSELECT=both ./ModularBot
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#
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# The mode itself is derived from SERVER truth (`getEnemyCount()`), never from
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# the tracker's known-enemy count, by `rackMode` in virtual_bullets — the same
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# transition the radar uses.
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const
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RackGunNames*: array[16, string] = [
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"HEADON", "LINEAR", "TSETLIN", "CIRCULAR", "GUESSFACTOR", "PATTERN",
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"WALLBOUNCE", "ACCEL", "STOPSHOT", "DISPLACE", "AVGLEAD", "DECAYGF",
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"KNN", "TMSELECT", "TMPATTERN", "TMHORIZON"]
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RackEnvPrefix* = "TR_RACK_"
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## SHIPPED DEFAULT: `onlyPattern`. Pattern (id 5) is admitted in both racks;
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## every other gun is `off`. The selection mechanism is untouched and remains
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## fully functional — only the rack's membership changed. Re-enable any gun
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## with `TR_RACK_<GUN>`, or restore the old full rack with the one-liner in the
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## header comment. TMPATTERN (id 14) stays `off`: registered and forceable but
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## it never spawns a virtual bullet unless explicitly enabled, so the shared
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## VirtualTracker ring head — and every other gun's learning order — is
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## unchanged.
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DefaultRackMembership*: array[16, RackMembership] = [
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rmOff, # 0 HEADON — off (measured: worst over-selected gun)
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rmOff, # 1 LINEAR — off
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rmOff, # 2 TSETLIN — off
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rmOff, # 3 CIRCULAR — off
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rmOff, # 4 GUESSFACTOR — off
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rmBoth, # 5 PATTERN — the only admitted gun (best single gun in general)
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rmOff, # 6 WALLBOUNCE — off
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rmOff, # 7 ACCEL — off
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rmOff, # 8 STOPSHOT — off
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rmOff, # 9 DISPLACE — off
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rmOff, # 10 AVGLEAD — off
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rmOff, # 11 DECAYGF — off
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rmOff, # 12 KNN — off
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rmOff, # 13 TMSELECT — off
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rmOff, # 14 TMPATTERN — off (already shipped off; TM pattern gun)
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rmOff] # 15 TMHORIZON — off (horizon-based TM corrector; expected to lose)
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proc parseRackMembership*(value: string): RackMembership =
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## Parse a `TR_RACK_<GUN>` value. Empty / unknown values fall back to the
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## shipped `both` and warn on stderr, so a typo cannot silently move a gun and
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## a bad value cannot take the bot down.
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case value.strip().toLowerAscii()
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of "", "both", "any": rmBoth
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of "1v1", "only1v1", "1v1only", "single", "lock": rmOnly1v1
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of "melee", "onlymelee", "multi": rmOnlyMelee
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of "off", "none", "disabled", "disable": rmOff
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else:
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stderr.writeLine("[gun_harness] unknown " & RackEnvPrefix & "<GUN>='" & value &
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"'; falling back to 'both' (valid: both|1v1|melee|off)")
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rmBoth
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proc loadRackMembership*(): array[len(RackGunNames), RackMembership] =
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## Default table plus every `TR_RACK_<GUN>` override. A proc (not inlined into
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## the `let`) so the unit test can exercise env parsing in-process.
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result = DefaultRackMembership
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for i in 0..<len(RackGunNames):
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let key = RackEnvPrefix & RackGunNames[i]
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let v = getEnv(key, "")
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if v.len > 0:
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result[i] = parseRackMembership(v)
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let ActiveRackMembership* = loadRackMembership()
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## Process-wide rack table, frozen at startup.
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proc rackMembershipName*(m: RackMembership): string =
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case m
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of rmBoth: "both"
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of rmOnly1v1: "1v1"
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of rmOnlyMelee: "melee"
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of rmOff: "off"
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proc rackModeName*(m: RackMode): string =
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case m
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of rm1v1: "1v1"
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of rmMelee: "melee"
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proc rackOverrides*(membership: openArray[RackMembership]): string =
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## Compact `GUN:mode,GUN:mode` list of entries that differ from the shipped
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## default table. Empty when the rack is at its default.
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for i in 0..<min(len(RackGunNames), membership.len):
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if membership[i] != rmBoth:
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if result.len > 0: result.add ","
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result.add RackGunNames[i] & ":" & rackMembershipName(membership[i])
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proc rackActive*(membership: openArray[RackMembership], mode: RackMode): string =
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## Comma-separated gun names admitted in `mode` (empty set prints as
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## `FULL` — the graceful-degradation fallback).
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for i in 0..<min(len(RackGunNames), membership.len):
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if membership[i].admits(mode):
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if result.len > 0: result.add ","
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result.add RackGunNames[i]
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if result.len == 0: result = "FULL"
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const
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## ── Range-aware firing gate ────────────────────────────────────────────────
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## A real shot departs with whatever misalignment the gun had at fire time,
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## while a virtual bullet is spawned exactly on the prediction and carries zero
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## aim error. At distance `d` the target subtends an angular half-width of
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## `atan(BotRadius / d)`, so a fixed degree threshold is simultaneously too
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## loose at long range (throws away shots that cannot hit) and too tight up
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## close (holds fire when the bot is already inside the hit cone).
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##
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## We therefore derive the tolerance from the target's angular radius:
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##
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## tolDeg = radToDeg(arctan(BotRadius * SafetyFactor / distPx))
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##
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## clamped to [MinAimThresholdDeg, MaxAimThresholdDeg].
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##
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## SafetyFactor shrinks/expands the accepted cone: 1.0 == the full geometric
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## half-width, < 1.0 is stricter. Fitted empirically from real-shot data
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## (Task A, 2611 real shots behind a wide-open 20 deg measurement gate).
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## The geometric model is only weakly identified: prediction error dominates
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## the hit rate, and the measured 50%-hit knee is noisy (0.9-1.4x the
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## geometric cone at 200-800 px; the 400-600 px bucket is ill-defined because
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## its baseline hit rate is already ~50%). Simulating the gate directly on the
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## measurement data showed 0.6 Pareto-dominates the old fixed 2.0 deg gate
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## (61.4% vs 60.2% hit rate with MORE shots), and the live sweep confirms the
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## observed preference for tighter gates. 0.6 is the shipped compromise:
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## tighter than the raw geometry while still loosening close range.
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SafetyFactor* = 0.6
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## Floor: keeps the tolerance strictly positive so a perfectly aligned gun can
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## always fire at any range, and guards the gate against collapsing to 0
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## (a never-fire deadlock) at extreme distances.
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MinAimThresholdDeg* = 0.05
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## Ceiling: at point-blank range the geometric cone grows without bound; a
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## >10 deg misalignment is a coin toss even at ~100 px, so cap it here.
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MaxAimThresholdDeg* = 10.0
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proc aimToleranceDeg*(distPx: float): float =
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## Angular half-width (deg) the gun may be off by and still plausibly hit a
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## target `distPx` px away, scaled by SafetyFactor and clamped.
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##
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## Degenerate distances (0 or unavailable) fall back to the ceiling rather than
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## dividing by zero; NaN is treated the same way (the `not (distPx > 0.0)`
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## test is false for NaN). +Inf falls through to arctan(0) == 0 and then the
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## floor, which is correct: an infinitely distant target is a point.
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if not (distPx > 0.0): return MaxAimThresholdDeg
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result = radToDeg(arctan(BotRadius * SafetyFactor / distPx))
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if result < MinAimThresholdDeg: result = MinAimThresholdDeg
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elif result > MaxAimThresholdDeg: result = MaxAimThresholdDeg
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proc aimAngle*(selfX, selfY, targetX, targetY: float): float =
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## Absolute bearing in degrees (0=East, CCW+) toward (targetX, targetY).
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result = radToDeg(arctan2(targetY - selfY, targetX - selfX))
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proc shouldFire*(currentGunDir, targetAngle, gunHeat, distPx: float): bool =
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## Returns true when the gun is within the range-aware angular tolerance and
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## cool enough to fire. `distPx` is the distance (px) to the aim point.
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var delta = (targetAngle - currentGunDir) mod 360.0
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if delta > 180.0: delta -= 360.0
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elif delta < -180.0: delta += 360.0
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abs(delta) <= aimToleranceDeg(distPx) and gunHeat <= 0.0
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proc selectShotPolicy*(t: var VirtualTracker, targetId = -1, tick = 0,
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dist = 0.0, selfEnergy = 100.0,
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enemyEnergy = 100.0,
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ramming = false,
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rackMode: RackMode = rm1v1,
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membership: openArray[RackMembership] = []
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): (GunId, int, float, PowerCap) =
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## `selectShot` plus the energy-aware power-policy decision, so a caller can
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## log the cap and its reason (see `applyPowerPolicy` in virtual_bullets).
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##
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## `dist` is the current distance (px) to the target, `selfEnergy` our own
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## energy and `enemyEnergy` the target's remaining energy (drives the
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## finishing cap); `ramming` exempts the caps (the movement code's `shouldRam`
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## is the single source of truth). The policy is applied identically wherever
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## this is called, so live and any offline caller cannot diverge.
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##
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## `rackMode` is the server-truth enemy-count mode (`rackMode`); `membership`
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## is the process-wide `TR_RACK_*` table, passed by the live bot. An empty
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## membership admits every gun (the pre-change behaviour).
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let gunId = t.selectGun(targetId, tick,
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rackMode = rackMode, membership = membership)
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let (prefBin, preferred) = t.bestPower(gunId, targetId)
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# pEst / pRef mirror `bestPower`'s own fitness source (per-target when data
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# exists, else the deterministic aggregate). An empty bin carries no rate of
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# its own, so it borrows the gun's aggregate — the same "no data" case the
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# policy documents.
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let fit = t.fitnessFor(targetId)
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let pRef = if PowerRefFixed > 0.0: PowerRefFixed
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else: gunRate(fit[gunId], pooled = true)
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let pEst =
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if fit[gunId].bins[prefBin].count == 0: pRef
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else: fit[gunId].bins[prefBin].hitRate()
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let dec = applyPowerPolicy(preferred, dist, selfEnergy, pEst, pRef, ramming,
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enemyEnergy = enemyEnergy)
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result = (gunId, binIndexForPower(dec.power), dec.power, dec)
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proc selectShot*(t: var VirtualTracker, targetId = -1, tick = 0,
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dist = 0.0, selfEnergy = 100.0,
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enemyEnergy = 100.0,
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ramming = false,
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rackMode: RackMode = rm1v1,
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membership: openArray[RackMembership] = []): (GunId, int, float) =
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## Returns (gunId, powerBinIdx, power) — the shot to take this tick.
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## Pass targetId to pick the best gun for that specific enemy. `tick` drives
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## the minimum-dwell hysteresis (see `selectGun`). `dist`/`selfEnergy`/
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## `enemyEnergy`/`ramming` feed the energy-aware power cap (`TR_POWER_POLICY`);
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## defaults keep every existing caller compiling, and `TR_POWER_POLICY=0`
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## reproduces the uncapped `bestPower` preference. Use `selectShotPolicy` when
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## the cap/reason is needed.
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let (gunId, binIdx, power, _) =
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t.selectShotPolicy(targetId, tick, dist, selfEnergy,
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enemyEnergy = enemyEnergy, ramming = ramming,
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rackMode = rackMode, membership = membership)
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result = (gunId, binIdx, power)
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