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