daniel156161
3a9af3f54d
- Split active strategies, reusable engine code, core classes, and legacy snakes. - Replace implicit snake imports with explicit module registrations. - Extract Prism duel, spatial, and survival behavior into focused mixins. - Improve duel scoring with food races, pressure, caches, and depth metrics. - Add deterministic arena scenarios and paired seeded engine tournaments. - Expand benchmark telemetry and bump Prism to version 1.3.0. - Update documentation and tests for the new package layout and tooling.
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
"""Reusable compact duel-search integration for Apex-style snakes."""
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from __future__ import annotations
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from snakes.engine.duel_search import BitboardDuelSearch
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class BitboardDuelMixin:
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def _new_duel_search(
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self, food_set: set, hazard_set: set, hazard_count: dict,
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hazard_damage: int, width: int, height: int, deadline: float | None,
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) -> BitboardDuelSearch:
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if self._duel_search_context is None:
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self._duel_search_context = BitboardDuelSearch(
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board=self._get_bb(width, height),
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food=food_set,
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hazards=hazard_set,
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hazard_count=hazard_count,
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hazard_damage=hazard_damage,
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deadline=deadline,
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)
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return self._duel_search_context
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def _minimax_candidate_id(
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self, my_body: list, enemy_body: list, my_target: tuple[int, int],
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food_set: set, hazard_set: set,
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my_health: int, enemy_health: int, hazard_damage: int, hazard_count: dict,
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width: int, height: int, max_depth: int, alpha: float, beta: float,
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deadline: float | None, previous_hazard_set: set | None = None,
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) -> tuple[float, int]:
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"""Resolve our selected move and every enemy reply simultaneously."""
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search = self._new_duel_search(
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food_set, hazard_set, hazard_count, hazard_damage,
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width, height, deadline,
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)
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adaptive_depth = max_depth
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remaining = self._remaining_ms(deadline)
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if remaining > 250:
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adaptive_depth = min(7, max_depth + 1)
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elif remaining < 120:
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adaptive_depth = min(max_depth, 2)
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return search.search_candidate(
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my_body=my_body,
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enemy_body=enemy_body,
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my_target=my_target,
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my_health=my_health,
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enemy_health=enemy_health,
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max_depth=adaptive_depth,
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previous_hazards=previous_hazard_set if previous_hazard_set is not None else hazard_set,
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)
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def _minimax_sim_id(
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self, my_body: list, enemy_body: list, food_set: set, hazard_set: set,
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my_health: int, enemy_health: int, hazard_damage: int, hazard_count: dict,
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width: int, height: int, max_depth: int, alpha: float, beta: float,
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deadline: float | None, previous_hazard_set: set | None = None,
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) -> tuple[float, int]:
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"""Run iterative deepening with one reusable compact search context."""
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search = self._new_duel_search(
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food_set, hazard_set, hazard_count, hazard_damage,
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width, height, deadline,
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)
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return search.search(
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my_body=my_body,
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enemy_body=enemy_body,
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my_health=my_health,
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enemy_health=enemy_health,
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max_depth=max_depth,
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previous_hazards=previous_hazard_set if previous_hazard_set is not None else hazard_set,
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)
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def _minimax_sim(
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self, my_body: list, enemy_body: list, food_set: set, hazard_set: set,
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my_health: int, enemy_health: int, hazard_damage: int, hazard_count: dict,
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width: int, height: int, depth: int, alpha: float, beta: float,
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deadline: float | None, previous_hazard_set: set | None = None,
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) -> float:
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"""Compatibility entry point for tests and callers requesting one depth."""
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search = self._new_duel_search(
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food_set, hazard_set, hazard_count, hazard_damage,
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width, height, deadline,
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)
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return search.search_depth(
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my_body=my_body,
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enemy_body=enemy_body,
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my_health=my_health,
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enemy_health=enemy_health,
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depth=depth,
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previous_hazards=previous_hazard_set if previous_hazard_set is not None else hazard_set,
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)
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