AI-Project/survival/graph_search.py

114 lines
3.6 KiB
Python

from enum import Enum
from queue import PriorityQueue
from survival import GameMap
from survival.components.position_component import PositionComponent
from survival.enums import Direction
class Action(Enum):
ROTATE_LEFT = 0
ROTATE_RIGHT = 1
MOVE = 2
class State:
def __init__(self, position: tuple[int, int], direction: Direction):
self.position = position
self.direction = direction
class Node:
def __init__(self, state: State, parent=None, action=None, cost=None):
self.state = state
self.parent = parent
self.action = action
self.cost = cost
def __lt__(self, other):
return self.cost < other.cost
def __eq__(self, other):
return self.cost == other.cost
def get_moved_position(position: tuple[int, int], direction: Direction):
vector = Direction.get_vector(direction)
return position[0] + vector[0], position[1] + vector[1]
def get_states(state: State, game_map: GameMap) -> list[tuple[Action, State, int]]:
states = list()
states.append((Action.ROTATE_LEFT, State(state.position, state.direction.rotate_left(state.direction)), 1))
states.append((Action.ROTATE_RIGHT, State(state.position, state.direction.rotate_right(state.direction)), 1))
target_position = get_moved_position(state.position, state.direction)
if not game_map.is_colliding(target_position):
states.append((Action.MOVE, State(target_position, state.direction), game_map.get_cost(target_position)))
return states
def build_path(node: Node):
actions = [node.action]
parent = node.parent
while parent is not None:
if parent.action is not None:
actions.append(parent.action)
parent = parent.parent
actions.reverse()
return actions
def heuristic(new_node: Node, goal: tuple[int, int]):
return abs(new_node.state.position[0] - goal[0]) + abs(new_node.state.position[1] - goal[1])
def graph_search(game_map: GameMap, start: PositionComponent, goal: tuple):
fringe = PriorityQueue()
explored = list()
explored_states = set()
fringe_states = set() # Stores positions and directions of states
start = State(start.grid_position, start.direction)
fringe.put((0, Node(start, cost=0)))
fringe_states.add((tuple(start.position), start.direction))
while True:
# No solutions found
if fringe.empty():
return []
node = fringe.get()
node_priority = node[0]
node = node[1]
fringe_states.remove((tuple(node.state.position), node.state.direction))
# Check goal
if node.state.position == goal:
return build_path(node)
explored.append(node)
explored_states.add((tuple(node.state.position), node.state.direction))
# Get all possible states
for state in get_states(node.state, game_map):
sub_state = (tuple(state[1].position), state[1].direction)
new_node = Node(state=state[1],
parent=node,
action=state[0],
cost=(state[2] + node.cost))
priority = new_node.cost + heuristic(new_node, goal)
if sub_state not in fringe_states and sub_state not in explored_states:
fringe.put((priority, new_node))
fringe_states.add((tuple(new_node.state.position), new_node.state.direction))
elif sub_state in fringe_states and node.cost > new_node.cost:
fringe.get(node)
fringe.put((priority, new_node))
fringe_states.add((tuple(new_node.state.position), new_node.state.direction))