WMICraft/algorithms/a_star.py

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from __future__ import annotations
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import heapq
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from dataclasses import dataclass, field
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from typing import Tuple, Optional, List
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from algorithms.genetic.const import MAP_ALIASES
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from common.constants import ROWS, COLUMNS, LEFT, RIGHT, UP, DOWN
from common.helpers import directions
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EMPTY_FIELDS = [MAP_ALIASES.get("SAND"), MAP_ALIASES.get("GRASS"), ' ']
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TURN_LEFT = 'TURN_LEFT'
TURN_RIGHT = 'TURN_RIGHT'
FORWARD = 'FORWARD'
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@dataclass
class State:
position: Tuple[int, int]
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direction: str
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def __eq__(self, other: State) -> bool:
return other.position == self.position and self.direction == other.direction
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def __lt__(self, state):
return self.position < state.position
def __hash__(self) -> int:
return hash(self.position)
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@dataclass
class Node:
state: State
parent: Optional[Node]
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action: Optional[str]
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grid: List[List[str]]
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cost: int = field(init=False)
depth: int = field(init=False)
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def __lt__(self, node) -> None:
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return self.state < node.state
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def __post_init__(self) -> None:
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if self.grid[self.state.position[0]][self.state.position[1]] == 'g':
self.cost = 1 if not self.parent else self.parent.cost + 1
else:
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self.cost = 2 if not self.parent else self.parent.cost + 2
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self.depth = 0 if not self.parent else self.parent.depth + 1
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def __hash__(self) -> int:
return hash(self.state)
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def expand(node: Node, grid: List[List[str]]) -> List[Node]:
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return [child_node(node=node, action=action, grid=grid) for action in actions(node.state, grid)]
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def child_node(node: Node, action: str, grid: List[List[str]]) -> Node:
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next_state = result(state=node.state, action=action)
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return Node(state=next_state, parent=node, action=action, grid=grid)
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def next_position(current_position: Tuple[int, int], direction: str) -> Tuple[int, int]:
next_row, next_col = directions[direction]
row, col = current_position
return next_row + row, next_col + col
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def valid_move(position: Tuple[int, int], grid: List[List[str]]) -> bool:
row, col = position
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return grid[row][col] in EMPTY_FIELDS
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def actions(state: State, grid: List[List[str]]) -> List[str]:
possible_actions = [FORWARD, TURN_LEFT, TURN_RIGHT]
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row, col = state.position
direction = state.direction
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if direction == UP and row == 0:
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remove_forward(possible_actions)
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if direction == DOWN and row == ROWS - 1:
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remove_forward(possible_actions)
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if direction == LEFT and col == 0:
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remove_forward(possible_actions)
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if direction == RIGHT and col == COLUMNS - 1:
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remove_forward(possible_actions)
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if FORWARD in possible_actions and not valid_move(next_position(state.position, direction), grid):
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remove_forward(possible_actions)
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return possible_actions
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def remove_forward(possible_actions: List[str]) -> None:
if FORWARD in possible_actions:
possible_actions.remove(FORWARD)
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def result(state: State, action: str) -> State:
next_state = State(state.position, state.direction)
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if state.direction == UP:
if action == TURN_LEFT:
next_state.direction = LEFT
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elif action == TURN_RIGHT:
next_state.direction = RIGHT
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elif action == FORWARD:
next_state.position = next_position(state.position, UP)
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elif state.direction == DOWN:
if action == TURN_LEFT:
next_state.direction = RIGHT
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elif action == TURN_RIGHT:
next_state.direction = LEFT
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elif action == FORWARD:
next_state.position = next_position(state.position, DOWN)
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elif state.direction == LEFT:
if action == TURN_LEFT:
next_state.direction = DOWN
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elif action == TURN_RIGHT:
next_state.direction = UP
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elif action == FORWARD:
next_state.position = next_position(state.position, LEFT)
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elif state.direction == RIGHT:
if action == TURN_LEFT:
next_state.direction = UP
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elif action == TURN_RIGHT:
next_state.direction = DOWN
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elif action == FORWARD:
next_state.position = next_position(state.position, RIGHT)
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return next_state
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def goal_test(state: State, goal_list: List[Tuple[int, int]]) -> bool:
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return state.position in goal_list
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def h(state: State, goal: Tuple[int, int]) -> int:
"""heuristics that calculates Manhattan distance between current position and goal"""
x1, y1 = state.position
x2, y2 = goal
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return abs(x1 - x2) + abs(y1 - y2)
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def f(current_node: Node, goal: Tuple[int, int]) -> int:
"""f(n) = g(n) + h(n), g stands for current cost, h for heuristics"""
return current_node.cost + h(state=current_node.state, goal=goal)
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def get_path_from_start(node: Node) -> List[str]:
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path = [node.action]
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while node.parent is not None:
node = node.parent
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if node.action:
path.append(node.action)
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path.reverse()
return path
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def a_star(state: State, grid: List[List[str]], goals: List[Tuple[int, int]]) -> List[str]:
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node = Node(state=state, parent=None, action=None, grid=grid)
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frontier = list()
heapq.heappush(frontier, (f(node, goals[0]), node))
explored = set()
while frontier:
r, node = heapq.heappop(frontier)
if goal_test(node.state, goals):
return get_path_from_start(node)
explored.add(node.state)
for child in expand(node, grid):
p = f(child, goals[0])
if child.state not in explored and (p, child) not in frontier:
heapq.heappush(frontier, (p, child))
elif (r, child) in frontier and r > p:
heapq.heappush(frontier, (p, child))
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return []