58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
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import queue
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from typing import List
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from data.Direction import Direction
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from data.GameConstants import GameConstants
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from decision.ActionType import ActionType
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from util.PathDefinitions import GridLocation
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from util.PriorityQueue import PriorityQueue
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class PathFinderState:
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def __init__(self, agent_position: GridLocation, agent_direction: Direction, cost: float):
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super().__init__()
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self.agent_position = agent_position
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self.agent_direction = agent_direction
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self.cost = cost
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self.last_action: ActionType
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self.action_taken: List[ActionType]
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def getActionTaken(self):
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return self.getActionTaken()
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#
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#
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#
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class PathFinderOnStates:
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def __init__(self, game_constants: GameConstants, goal: GridLocation, root_state: PathFinderState):
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super().__init__()
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self.game_constants = game_constants
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self.goal = goal
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self.queue = PriorityQueue()
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self.queue.put(root_state, root_state.cost)
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def heuristic(self) -> float:
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# tutaj mozna uzyc heury np. manhatan distance (zmodyfikowany bo masz obroty a to zmienia oplacalnosc)
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pass
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def evaluate(self, state: PathFinderState) -> float:
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# koszt dojscia do danego stanu
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pass
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def expansion(self, state: PathFinderState) -> List[PathFinderState]:
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# dla stanu sprawdzamy jakie akcje z tego miejsca mozemy podjac (ActionType)
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pass
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def getActionList(self) -> List[ActionType]:
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best_state: PathFinderState = self.queue.get()
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while best_state.agent_position != self.goal or self.queue.empty():
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# dodajesz do kolejki stany z expansion (po cost)
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best_state = self.queue.get()
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return best_state.getActionTaken()
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