pathFinder on states init

test/dlaMarcina.py

@@ -0,0 +1,58 @@
+import queue
+from typing import List
+
+from data.Direction import Direction
+from data.GameConstants import GameConstants
+from decision.ActionType import ActionType
+from util.PathDefinitions import GridLocation
+from util.PriorityQueue import PriorityQueue
+
+
+
+class PathFinderState:
+
+    def __init__(self, agent_position: GridLocation, agent_direction: Direction, cost: float):
+        super().__init__()
+        self.agent_position = agent_position
+        self.agent_direction = agent_direction
+        self.cost = cost
+        self.last_action: ActionType
+        self.action_taken: List[ActionType]
+
+    def getActionTaken(self):
+        return self.getActionTaken()
+
+
+#
+#
+#
+
+
+class PathFinderOnStates:
+    def __init__(self, game_constants: GameConstants, goal: GridLocation, root_state: PathFinderState):
+        super().__init__()
+        self.game_constants = game_constants
+        self.goal = goal
+        self.queue = PriorityQueue()
+        self.queue.put(root_state, root_state.cost)
+
+    def heuristic(self) -> float:
+        # tutaj mozna uzyc heury np. manhatan distance (zmodyfikowany bo masz obroty a to zmienia oplacalnosc)
+        pass
+
+    def evaluate(self, state: PathFinderState) -> float:
+        # koszt dojscia do danego stanu
+        pass
+
+    def expansion(self, state: PathFinderState) -> List[PathFinderState]:
+        # dla stanu sprawdzamy jakie akcje z tego miejsca mozemy podjac (ActionType)
+        pass
+
+    def getActionList(self) -> List[ActionType]:
+        best_state: PathFinderState = self.queue.get()
+
+        while best_state.agent_position != self.goal or self.queue.empty():
+        #     dodajesz do kolejki stany z expansion (po cost)
+            best_state = self.queue.get()
+
+        return best_state.getActionTaken()