Machine_learning_2023/AI_brain/go_any_direction.py
2023-05-01 23:59:15 +02:00

81 lines
2.4 KiB
Python

from domain.commands.vacuum_move_command import VacuumMoveCommand
from domain.world import World
class State:
def __init__(self, x, y):
self.x = x
self.y = y
def __hash__(self):
return hash((self.x, self.y))
def __eq__(self, other):
return self.x == other.x and self.y == other.y
class GoAnyDirectionBFS:
def __init__(self, world: World, start_state: State, goal_state: State):
self.start_state = start_state
self.goal_state = goal_state
self.visited = set()
self.parent = {}
self.actions = []
self.path = []
self.world = world
self.queue = []
def search(self):
self.queue.append(self.start_state)
self.visited.add(self.start_state)
while self.queue:
state = self.queue.pop(0)
if state == self.goal_state:
self.actions = self.get_actions()
self.path = self.get_path()
return True
for successor in self.successors(state):
if successor not in self.visited:
self.visited.add(successor)
self.parent[successor] = state
self.queue.append(successor)
return False
def successors(self, state):
new_successors = [
State(state.x + dx, state.y + dy)
for dx, dy in [(1, 0), (0, 1), (-1, 0), (0, -1)]
if self.world.accepted_move(state.x + dx, state.y + dy)
]
return new_successors
def get_actions(self):
actions = []
state = self.goal_state
while state != self.start_state:
parent_state = self.parent[state]
dx = state.x - parent_state.x
dy = state.y - parent_state.y
if dx == 1:
actions.append("RIGHT")
elif dx == -1:
actions.append("LEFT")
elif dy == 1:
actions.append("DOWN")
elif dy == -1:
actions.append("UP")
state = parent_state
actions.reverse()
return actions
def get_path(self):
path = []
state = self.goal_state
while state != self.start_state:
path.append((state.x, state.y))
state = self.parent[state]
path.append((self.start_state.x, self.start_state.y))
path.reverse()
return path