sztuczna_inteligencja_2023_smieciarka/bfs.py

from agentState import AgentState
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, PriorityQueue
from turnCar import turn_left_orientation, turn_right_orientation


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.predicted_cost = cost


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 == 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 = current.succ_list.copy()
                new_list.append(s)
                queue.put(SuccessorList(new_list))

    return []


def extract_actions(successors: SuccessorList) -> list[AgentActionType]:
    output: list[AgentActionType] = []
    for s in successors.succ_list:
        if s.action != AgentActionType.UNKNOWN:
            output.append(s.action)
    return output


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(succ: Successor, city: City, grid: Dict[Tuple[int, int], GridCellType]) -> Successor:
    position = get_next_cell(succ.state)
    if position is None:
        return None

    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]:
    x, y = state.position
    orientation = state.orientation

    if orientation == AgentOrientation.UP:
        if y - 1 < 1:
            return None
        return x, y - 1
    elif orientation == AgentOrientation.DOWN:
        if y + 1 > 27:
            return None
        return x, y + 1
    elif orientation == AgentOrientation.LEFT:
        if x - 1 < 1:
            return None
        return x - 1, y
    elif x + 1 > 27:
        return None
    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 KeyError:
        return False


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
    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 KeyError:
        return False


def _heuristics(position: Tuple[int, int], city: City):
    min_distance: int = 300
    found_nonvisited: bool = False
    for can in city.cans:
        if can.is_visited:
            continue
        found_nonvisited = True
        distance = 3 * (abs(position[0] - can.position[0]) + abs(position[1] - can.position[1]))
        if distance < min_distance:
            min_distance = distance
    if found_nonvisited:
        return min_distance
    return -1