Files
pf1e-simulator/src/pf1e_simulator/runner.py
T
ctan a21e234b41 feat(runner): Monte Carlo runner, closed-form metrics and balance CLI
Adds the S6 layer on top of the deterministic combat engine:
- metrics.py: closed-form binomial statistics (win rate, standard error,
  3-sigma confidence band) with clamped degenerate proportions.
- runner.py: EncounterSpec/Side definitions, per-run SeededRng streams,
  global duplicate-id disambiguation, attrition averages per combatant.
- cli.py: pf1e-sim entry point producing a French balance report with
  win rates, 3-sigma bands, draws, rounds, and attrition.
- Movement fix: greedy straight-line heuristic could oscillate at walls;
  _step_toward now follows the true shortest path via an unbounded
  Dijkstra cost field from the target (Grid.reachable budget=None).
- Regression tests: melee unit routes around a wall and engages; grid
  unbounded-budget coverage.
2026-08-17 22:49:50 +02:00

172 lines
5.6 KiB
Python

"""Monte Carlo runner: many deterministic battles aggregated into balance metrics.
Each run builds fresh combatant states (duplicate ids get -N suffixes), places
each side inside its deployment zone, and plays one battle with a dedicated
SeededRng stream derived from the encounter seed. Results are aggregated into
an EncounterReport: wins per side, draws, average rounds, and per-combatant
attrition averages. All statistical helpers live in metrics.py.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from pf1e_simulator.combat import Policy
from pf1e_simulator.grid import Grid
from pf1e_simulator.map import MapSpec
from pf1e_simulator.models import Combatant
from pf1e_simulator.combat import CombatantState, CombatantStats, CombatEngine
from pf1e_simulator.grid import Grid
from pf1e_simulator.map import zone_cells
from pf1e_simulator.metrics import win_rate, win_rate_band, win_rate_sigma
from pf1e_simulator.rng import SeededRng
@dataclass(frozen=True)
class Side:
"""One faction in an encounter: combatants deployed inside one zone letter."""
name: str
combatants: tuple[Combatant, ...]
zone: str
@dataclass(frozen=True)
class EncounterSpec:
"""A full encounter definition: map, sides, and a battle round cap."""
map: MapSpec
sides: tuple[Side, ...]
round_cap: int = 100
@dataclass(frozen=True)
class CombatantAttrition:
"""Average per-combatant outcomes over a Monte Carlo run."""
hits: float
crits: float
damage_dealt: float
damage_taken: float
@dataclass(frozen=True)
class EncounterReport:
"""Aggregated results of a Monte Carlo encounter."""
runs: int
seed: int
wins: dict[str, int]
draws: int
avg_rounds: float
attrition: dict[str, CombatantAttrition]
def win_rate(self, side: str) -> float:
"""Fraction of runs won by `side` (closed form, see metrics)."""
return win_rate(self.wins.get(side, 0), self.runs)
def win_rate_sigma(self, side: str) -> float:
"""Standard error of the side's win rate: sqrt(p(1-p)/n)."""
return win_rate_sigma(self.win_rate(side), self.runs)
def win_rate_band(self, side: str) -> tuple[float, float]:
"""3-sigma confidence band for the side's win rate."""
return win_rate_band(self.win_rate(side), self.runs)
def build_states(spec: EncounterSpec) -> list[CombatantState]:
"""Fresh states for one battle; duplicate combatant ids get -N suffixes.
Combatants of a side fill the zone's cells in (row, col) order. A side with
more combatants than zone cells, or a zone with no cells, is a spec error.
"""
states: list[CombatantState] = []
seen: dict[str, int] = {}
for side in spec.sides:
cells = zone_cells(spec.map, side.zone)
if not cells:
msg = f"side {side.name!r}: zone {side.zone!r} has no cells"
raise ValueError(msg)
if len(cells) < len(side.combatants):
msg = (
f"side {side.name!r}: zone {side.zone!r} has {len(cells)} cells "
f"for {len(side.combatants)} combatants"
)
raise ValueError(msg)
for combatant, pos in zip(side.combatants, cells, strict=False):
count = seen.get(combatant.id, 0) + 1
seen[combatant.id] = count
cid = combatant.id if count == 1 else f"{combatant.id}-{count}"
effective = (
combatant
if cid == combatant.id
else combatant.model_copy(update={"id": cid})
)
states.append(
CombatantState(
combatant=effective,
side=side.name,
pos=pos,
hp=effective.hp_max,
)
)
return states
def run_encounter(
spec: EncounterSpec,
runs: int,
seed: int,
*,
policy: Policy | None = None,
) -> EncounterReport:
"""Run `runs` battles (one SeededRng per run) and aggregate balance metrics."""
if runs <= 0:
msg = "runs must be positive"
raise ValueError(msg)
grid = Grid.from_spec(spec.map)
states = build_states(spec)
ids = [s.combatant.id for s in states]
wins = {side.name: 0 for side in spec.sides}
draws = 0
total_rounds = 0
totals = {cid: CombatantStats() for cid in ids}
for index in range(runs):
if index > 0:
states = build_states(spec)
rng = SeededRng(seed + index)
result = CombatEngine(rng, grid, states, round_cap=spec.round_cap, policy=policy).run()
if result.winner is None:
draws += 1
else:
wins[result.winner] = wins.get(result.winner, 0) + 1
total_rounds += result.rounds
for cid, stats in result.stats.items():
current = totals[cid]
totals[cid] = CombatantStats(
hits=current.hits + stats.hits,
crits=current.crits + stats.crits,
damage_dealt=current.damage_dealt + stats.damage_dealt,
damage_taken=current.damage_taken + stats.damage_taken,
)
attrition = {
cid: CombatantAttrition(
hits=stats.hits / runs,
crits=stats.crits / runs,
damage_dealt=stats.damage_dealt / runs,
damage_taken=stats.damage_taken / runs,
)
for cid, stats in totals.items()
}
return EncounterReport(
runs=runs,
seed=seed,
wins=wins,
draws=draws,
avg_rounds=total_rounds / runs,
attrition=attrition,
)