Merge pull request 'A_star' (#25) from A_star into master

Reviewed-on: #25
Reviewed-by: Paweł Felcyn <pawfel1@st.amu.edu.pl>

agentState.py

@@ -7,4 +7,4 @@ class AgentState:
 
     def __init__(self, position: Tuple[int, int], orientation: AgentOrientation) -> None:
         self.orientation = orientation
-        self.position = position
+        self.position = position

bfs.py

@@ -1,112 +1,155 @@
 from agentState import AgentState
-from typing import Dict, Tuple
+from typing import Dict, Tuple, List
 from city import City
 from gridCellType import GridCellType
 from agentActionType import AgentActionType
 from agentOrientation import AgentOrientation
-from queue import Queue
+from queue import Queue, PriorityQueue
 from turnCar import turn_left_orientation, turn_right_orientation
 
-class Succ:
-    state: AgentState
-    action: AgentActionType
-    ##cost: int
 
-    def __init__(self, state: AgentState, action: AgentActionType) -> None:
+class Successor:
+
+    def __init__(self, state: AgentState, action: AgentActionType, cost: int, predicted_cost: int) -> None:
         self.state = state
         self.action = action
-        ##self.cost = cost
+        self.cost = cost
+        self.predicted_cost = cost
 
-def find_path_to_nearest_can(startState: AgentState, grid: Dict[Tuple[int, int], GridCellType]) -> list[AgentActionType]:
-    q: Queue[list[Succ]] = Queue()
-    visited: list[AgentState] = []
-    startStates: list[Succ] = [Succ(startState, AgentActionType.UNKNOWN)]
-    q.put(startStates)
-    while not q.empty():
-        currently_checked = q.get()
-        visited.append(currently_checked[-1].state)
-        if is_state_success(currently_checked[-1].state, grid):
-            return extract_actions(currently_checked)
-        successors = succ(currently_checked[-1].state)
+
+class SuccessorList:
+    succ_list: list[Successor]
+
+    def __init__(self, succ_list: list[Successor]) -> None:
+        self.succ_list = succ_list
+
+    def __gt__(self, other):
+        return self.succ_list[-1].predicted_cost > other.succ_list[-1].predicted_cost
+
+    def __lt__(self, other):
+        return self.succ_list[-1].predicted_cost < other.succ_list[-1].predicted_cost
+
+
+def find_path_to_nearest_can(startState: AgentState, grid: Dict[Tuple[int, int], GridCellType], city: City) -> List[
+    AgentActionType]:
+    visited: List[AgentState] = []
+    queue: PriorityQueue[SuccessorList] = PriorityQueue()
+    queue.put(SuccessorList([Successor(startState, AgentActionType.UNKNOWN, 0, _heuristics(startState.position, city))]))
+
+    while not queue.empty():
+        current = queue.get()
+        previous = current.succ_list[-1]
+        visited.append(previous.state)
+
+        if is_state_success(previous.state, grid):
+            return extract_actions(current)
+
+        successors = get_successors(previous, grid, city)
         for s in successors:
             already_visited = False
             for v in visited:
-                if v.position[0] == s.state.position[0] and v.position[1] == s.state.position[1] and s.state.orientation == v.orientation:
+                if v.position == s.state.position and v.orientation == s.state.orientation:
                     already_visited = True
                     break
             if already_visited:
                 continue
             if is_state_valid(s.state, grid):
-                new_list = currently_checked.copy()
+                new_list = current.succ_list.copy()
                 new_list.append(s)
-                q.put(new_list)
+                queue.put(SuccessorList(new_list))
+
     return []
 
-        
-        
-def extract_actions(successors: list[Succ]) -> list[AgentActionType]:
+
+def extract_actions(successors: SuccessorList) -> list[AgentActionType]:
     output: list[AgentActionType] = []
-    for s in successors:
+    for s in successors.succ_list:
         if s.action != AgentActionType.UNKNOWN:
             output.append(s.action)
     return output
 
-def succ(state: AgentState) -> list[Succ]:
-    result: list[Succ] = []
-    result.append(Succ(AgentState(state.position, turn_left_orientation(state.orientation)), AgentActionType.TURN_LEFT))
-    result.append(Succ(AgentState(state.position, turn_right_orientation(state.orientation)), AgentActionType.TURN_RIGHT))
-    state_succ = move_forward_succ(state)
-    if state_succ != None:
-        result.append(move_forward_succ(state))
+
+def get_successors(succ: Successor, grid: Dict[Tuple[int, int], GridCellType], city: City) -> List[Successor]:
+    result: List[Successor] = []
+
+    turn_left_cost = 1 + succ.cost
+    turn_left_state = AgentState(succ.state.position, turn_left_orientation(succ.state.orientation))
+    turn_left_heuristics = _heuristics(succ.state.position, city)
+    result.append(
+        Successor(turn_left_state, AgentActionType.TURN_LEFT, turn_left_cost, turn_left_cost + turn_left_heuristics))
+
+    turn_right_cost = 1 + succ.cost
+    turn_right_state = AgentState(succ.state.position, turn_right_orientation(succ.state.orientation))
+    turn_right_heuristics = _heuristics(succ.state.position, city)
+    result.append(
+        Successor(turn_right_state, AgentActionType.TURN_RIGHT, turn_right_cost,
+                  turn_right_cost + turn_right_heuristics))
+
+    state_succ = move_forward_succ(succ, city, grid)
+    if state_succ is not None:
+        result.append(state_succ)
+
     return result
 
-def move_forward_succ(state: AgentState) -> Succ:
-    position = get_next_cell(state)
-    if position == None:
+
+def move_forward_succ(succ: Successor, city: City, grid: Dict[Tuple[int, int], GridCellType]) -> Successor:
+    position = get_next_cell(succ.state)
+    if position is None:
         return None
-    return Succ(AgentState(position, state.orientation), AgentActionType.MOVE_FORWARD)
+
+    cost = get_cost_for_action(AgentActionType.MOVE_FORWARD, grid[position]) + succ.cost
+    predicted_cost = cost + _heuristics(position, city)
+    new_state = AgentState(position, succ.state.orientation)
+    return Successor(new_state, AgentActionType.MOVE_FORWARD, cost, predicted_cost)
 
 
 def get_next_cell(state: AgentState) -> Tuple[int, int]:
-    if state.orientation == AgentOrientation.UP:
-        if state.position[1] - 1 < 1:
+    x, y = state.position
+    orientation = state.orientation
+
+    if orientation == AgentOrientation.UP:
+        if y - 1 < 1:
             return None
-        return (state.position[0], state.position[1] - 1)
-    if state.orientation == AgentOrientation.DOWN:
-        if state.position[1] + 1 > 27:
+        return x, y - 1
+    elif orientation == AgentOrientation.DOWN:
+        if y + 1 > 27:
             return None
-        return (state.position[0], state.position[1] + 1)
-    if state.orientation == AgentOrientation.LEFT:
-        if state.position[0] - 1 < 1:
+        return x, y + 1
+    elif orientation == AgentOrientation.LEFT:
+        if x - 1 < 1:
             return None
-        return (state.position[0] - 1, state.position[1])
-    if state.position[0] + 1 > 27:
+        return x - 1, y
+    elif x + 1 > 27:
         return None
-    return (state.position[0] + 1, state.position[1])
+    else:
+        return x + 1, y
+
 
 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
-    except:
+    except KeyError:
         return False
 
+
 def get_cost_for_action(action: AgentActionType, cell_type: GridCellType) -> int:
-    if action == AgentActionType.TURN_LEFT or action == AgentActionType.TURN_RIGHT:
+    if action in [AgentActionType.TURN_LEFT, AgentActionType.TURN_RIGHT]:
         return 1
-    if cell_type == GridCellType.SPEED_BUMP:
-        if action == AgentActionType.MOVE_FORWARD:
-            return 10
+    if cell_type == GridCellType.SPEED_BUMP and action == AgentActionType.MOVE_FORWARD:
+        return 10
     if action == AgentActionType.MOVE_FORWARD:
         return 3
 
 
 def is_state_valid(state: AgentState, grid: Dict[Tuple[int, int], GridCellType]) -> bool:
-    try: 
-        return grid[state.position] == GridCellType.STREET_HORIZONTAL or grid[state.position] == GridCellType.STREET_VERTICAL or grid[state.position] == GridCellType.SPEED_BUMP
-    except:
+    try:
+        return grid[state.position] == GridCellType.STREET_HORIZONTAL or grid[
+            state.position] == GridCellType.STREET_VERTICAL or grid[state.position] == GridCellType.SPEED_BUMP
+    except KeyError:
         return False
-    
+
+
 def _heuristics(position: Tuple[int, int], city: City):
     min_distance: int = 300
     found_nonvisited: bool = False
@@ -120,4 +163,3 @@ def _heuristics(position: Tuple[int, int], city: City):
     if found_nonvisited:
         return min_distance
     return -1
-    

city.py

@@ -2,8 +2,9 @@ from typing import List, Dict, Tuple
 from garbageCan import GarbageCan
 from speedBump import SpeedBump
 from street import Street
-from gameContext import GameContext        
-        
+from gameContext import GameContext
+
+
 class City:
     cans: List[GarbageCan]
     bumps: List[SpeedBump]
@@ -11,17 +12,17 @@ class City:
     cans_dict: Dict[Tuple[int, int], GarbageCan] = {}
 
     def __init__(self) -> None:
-        self.nodes = []
+        self.cans = []
         self.streets = []
         self.bumps = []
 
     def add_can(self, can: GarbageCan) -> None:
-        self.nodes.append(can)
+        self.cans.append(can)
         self.cans_dict[can.position] = can
-    
+
     def add_street(self, street: Street) -> None:
         self.streets.append(street)
-        
+
     def add_bump(self, bump: SpeedBump) -> None:
         self.streets.append(bump)
 
@@ -33,11 +34,11 @@ class City:
     def _render_streets(self, game_context: GameContext) -> None:
         for street in self.streets:
             street.render(game_context)
-            
+
     def _render_nodes(self, game_context: GameContext) -> None:
-        for node in self.nodes:
+        for node in self.cans:
             node.render(game_context)
-            
+
     def _render_bumps(self, game_context: GameContext) -> None:
         for bump in self.bumps:
             bump.render(game_context)

movement.py

@@ -10,10 +10,11 @@ import pygame
 from bfs import find_path_to_nearest_can
 from agentState import AgentState
 
+
 def collect_garbage(game_context: GameContext) -> None:
     while True:
         start_agent_state = AgentState(game_context.dust_car.position, game_context.dust_car.orientation)
-        path = find_path_to_nearest_can(start_agent_state, game_context.grid)
+        path = find_path_to_nearest_can(start_agent_state, game_context.grid, game_context.city)
         if path == None or len(path) == 0:
             break
         move_dust_car(path, game_context)
@@ -22,6 +23,7 @@ def collect_garbage(game_context: GameContext) -> None:
         game_context.city.cans_dict[next_position].is_visited = True
     pass
 
+
 def move_dust_car(actions: list[AgentActionType], game_context: GameContext) -> None:
     for action in actions:
         street_position = game_context.dust_car.position
@@ -44,7 +46,6 @@ def move_dust_car(actions: list[AgentActionType], game_context: GameContext) ->
         pygame.display.update()
         time.sleep(0.15)
 
-        
 
 def calculate_next_position(car: GarbageTruck) -> Tuple[int, int]:
     if car.orientation == AgentOrientation.UP: