Machine_learning_2023/AI_brain/rotate_and_go_aStar.py

import heapq
from domain.world import World


class State:
    def __init__(self, x, y, direction=(1, 0), entity=None):
        self.x = x
        self.y = y
        self.direction = direction
        

    def __hash__(self):
        return hash((self.x, self.y))

    def __eq__(self, other):
        return (
            self.x == other.x
            and self.y == other.y
            and self.direction == other.direction
        )

    def heuristic(self, goal_state):
        return abs(self.x - goal_state.x) + abs(self.y - goal_state.y)


class Node:
    def __init__(self, state: State, g_score: int, goal_state: State):
        self.state = state
        self.g_score = g_score
        self.f_score = g_score + state.heuristic(goal_state)
        self.parent = None
        self.action = None

    def __lt__(self, other):
        return self.f_score < other.f_score


def action_sequence(node: Node):
    actions = []
    while node.parent:
        actions.append(node.action)
        node = node.parent
    actions.reverse()
    return actions


class RotateAndGoAStar:
    def __init__(self, world: World, start_state: State, goal_state: State):
        self.world = world
        self.start_state = start_state
        self.goal_state = goal_state
        self.fringe = []
        self.enqueued_states = set()
        self.explored = set()
        self.actions = []

    def get_g_score(self, state):
        return self.world.get_cost(state.x, state.y)

    def search(self):
        heapq.heappush(
            self.fringe, Node(self.start_state, 0, self.goal_state)
        )

        while self.fringe:
            elem = heapq.heappop(self.fringe)
            if self.is_goal(elem.state):
                self.actions = action_sequence(elem)
                return True
            self.explored.add(elem.state)

            for action, state in self.successors(elem.state):
                if state in self.explored:
                    continue

                new_g_score = new_g_score = elem.g_score + self.world.get_cost(state.x, state.y)
                if state not in self.enqueued_states:
                    next_node = Node(state, new_g_score, self.goal_state)
                    next_node.action = action
                    next_node.parent = elem
                    heapq.heappush(self.fringe, next_node)
                    self.enqueued_states.add(state)
                elif new_g_score < self.get_g_score(state):
                    for node in self.fringe:
                        if node.state == state:
                            node.g_score = new_g_score
                            node.f_score = (
                                new_g_score + node.state.heuristic(self.goal_state)
                            )
                            node.parent = elem
                            node.action = action
                            heapq.heapify(self.fringe)  
                            break

        return False

    def successors(self, state: State):
        new_successors = [
            ("RR", State(state.x, state.y, (-state.direction[1], state.direction[0]))),
            ("RL", State(state.x, state.y, (state.direction[1], -state.direction[0]))),
        ]
        next_x = state.x + state.direction[0]
        next_y = state.y + state.direction[1]
        if self.world.accepted_move(next_x, next_y):
            new_successors.append(
                ("GO", State(next_x, next_y, state.direction))
            )
        return new_successors
   
    def is_goal(self, state: State) -> bool:
        return (
                state.x == self.goal_state.x
                and state.y == self.goal_state.y )