bfs.py

@@ -8,16 +8,16 @@ from queue import Queue, PriorityQueue
 from turnCar import turn_left_orientation, turn_right_orientation
 
 
-class Successor:
+class Successor:  # klasa reprezentuje sukcesora, stan i akcję którą można po nim podjąć
 
     def __init__(self, state: AgentState, action: AgentActionType, cost: int, predicted_cost: int) -> None:
         self.state = state
         self.action = action
         self.cost = cost
-        self.predicted_cost = cost
+        self.predicted_cost = predicted_cost
 
 
-class SuccessorList:
+class SuccessorList:  # lista sukcesorów, czyli możliwych ścieżek po danym stanie
     succ_list: list[Successor]
 
     def __init__(self, succ_list: list[Successor]) -> None:
@@ -31,17 +31,17 @@ class SuccessorList:
 
 
 def find_path_to_nearest_can(startState: AgentState, grid: Dict[Tuple[int, int], GridCellType], city: City) -> List[
-    AgentActionType]:
+    AgentActionType]:  # znajduje ścieżkę do najbliższego kosza na smieci
     visited: List[AgentState] = []
-    queue: PriorityQueue[SuccessorList] = PriorityQueue()
+    queue: PriorityQueue[SuccessorList] = PriorityQueue()  # kolejka priorytetowa przechodująca listę sukcesorów
     queue.put(SuccessorList([Successor(startState, AgentActionType.UNKNOWN, 0, _heuristics(startState.position, city))]))
 
-    while not queue.empty():
+    while not queue.empty():  # dopóki kolejka nie jest pusta, pobiera z niej aktualny element
         current = queue.get()
         previous = current.succ_list[-1]
         visited.append(previous.state)
 
-        if is_state_success(previous.state, grid):
+        if is_state_success(previous.state, grid):  # jeśli ostatni stan w liście jest stanem końcowym (agent dotarł do śmietnika)
             return extract_actions(current)
 
         successors = get_successors(previous, grid, city)
@@ -61,7 +61,7 @@ def find_path_to_nearest_can(startState: AgentState, grid: Dict[Tuple[int, int],
     return []
 
 
-def extract_actions(successors: SuccessorList) -> list[AgentActionType]:
+def extract_actions(successors: SuccessorList) -> list[AgentActionType]:  # wyodrębnienie akcji z listy sukcesorów, z pominięciem uknown
     output: list[AgentActionType] = []
     for s in successors.succ_list:
         if s.action != AgentActionType.UNKNOWN:
@@ -70,7 +70,7 @@ def extract_actions(successors: SuccessorList) -> list[AgentActionType]:
 
 
 def get_successors(succ: Successor, grid: Dict[Tuple[int, int], GridCellType], city: City) -> List[Successor]:
-    result: List[Successor] = []
+    result: List[Successor] = []  # generuje następników dla danego stanu,
 
     turn_left_cost = 1 + succ.cost
     turn_left_state = AgentState(succ.state.position, turn_left_orientation(succ.state.orientation))
@@ -128,7 +128,7 @@ def get_next_cell(state: AgentState) -> Tuple[int, int]:
 def is_state_success(state: AgentState, grid: Dict[Tuple[int, int], GridCellType]) -> bool:
     next_cell = get_next_cell(state)
     try:
-        return grid[next_cell] == GridCellType.GARBAGE_CAN
+        return grid[next_cell] == GridCellType.GARBAGE_CAN  # agent dotarł do śmietnika
     except KeyError:
         return False
 
@@ -137,7 +137,7 @@ def get_cost_for_action(action: AgentActionType, cell_type: GridCellType) -> int
     if action in [AgentActionType.TURN_LEFT, AgentActionType.TURN_RIGHT]:
         return 1
     if cell_type == GridCellType.SPEED_BUMP and action == AgentActionType.MOVE_FORWARD:
-        return 10
+        return -10000
     if action == AgentActionType.MOVE_FORWARD:
         return 3