fix: actions

algorithms/a_star.py

@@ -1,40 +1,39 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from enum import Enum, unique
 from typing import Tuple, Optional, List
 
 from common.constants import ROWS, COLUMNS
 
 FREE_FIELD = ' '
+LEFT = 'LEFT'
+RIGHT = 'RIGHT'
+UP = 'UP'
+DOWN = 'DOWN'
 
+directions = {
+    LEFT: (0, -1),
+    RIGHT: (0, 1),
+    UP: (-1, 0),
+    DOWN: (1, 0)
+}
 
-@unique
-class Direction(Enum):
-    LEFT = (0, -1)
-    RIGHT = (0, 1)
-    UP = (-1, 0)
-    DOWN = (1, 0)
-
-
-@unique
-class Action(Enum):
-    TURN_LEFT = 'TURN_LEFT'
-    TURN_RIGHT = 'TURN_RIGHT'
-    FORWARD = 'FORWARD'
+TURN_LEFT = 'TURN_LEFT'
+TURN_RIGHT = 'TURN_RIGHT'
+FORWARD = 'FORWARD'
 
 
 @dataclass
 class State:
     position: Tuple[int, int]
-    direction: Direction
+    direction: str
 
 
 @dataclass
 class Node:
     state: State
     parent: Optional[Node]
-    action: Action
+    action: str
     cost: int = field(init=False)
     depth: int = field(init=False)
 
@@ -53,15 +52,15 @@ def expand(node: Node) -> List[Node]:
     return [child_node(node=node, action=action) for action in actions(node.state)]
 
 
-def child_node(node: Node, action: Action) -> Node:
+def child_node(node: Node, action: str) -> Node:
     next_state = result(state=node.state, action=action)
     return Node(state=next_state, parent=node, action=action)
 
 
-def next_position(current_position: Tuple[int, int], direction: Direction) -> Tuple[int, int]:
-    x1, y1 = direction.value
-    x2, y2 = current_position
-    return x1 + x2, y1 + y2
+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
 
 
 def valid_move(position: Tuple[int, int], grid: List[List[str]]) -> bool:
@@ -69,32 +68,32 @@ def valid_move(position: Tuple[int, int], grid: List[List[str]]) -> bool:
     return grid[row][col] == FREE_FIELD
 
 
-def actions(state: State, grid: List[List[str]]) -> List[Action]:
-    possible_actions = [Action.FORWARD, Action.TURN_LEFT, Action.TURN_RIGHT]
+def actions(state: State, grid: List[List[str]]) -> List[str]:
+    possible_actions = [FORWARD, TURN_LEFT, TURN_RIGHT]
     row, col = state.position
     direction = state.direction
 
-    if direction == Direction.UP and row == 0:
+    if direction == UP and row == 0:
         remove_forward(possible_actions)
-    if direction == Direction.DOWN and row == ROWS - 1:
+    if direction == DOWN and row == ROWS - 1:
         remove_forward(possible_actions)
-    if direction == Direction.LEFT and col == 0:
+    if direction == LEFT and col == 0:
         remove_forward(possible_actions)
-    if direction == Direction.RIGHT and col == COLUMNS - 1:
+    if direction == RIGHT and col == COLUMNS - 1:
         remove_forward(possible_actions)
 
-    if not valid_move(next_position(state.position, direction), grid):
+    if FORWARD not in possible_actions and not valid_move(next_position(state.position, direction), grid):
         remove_forward(possible_actions)
 
     return possible_actions
 
 
-def remove_forward(possible_actions: List[Action]) -> None:
-    if Action.FORWARD in possible_actions:
-        possible_actions.remove(Action.FORWARD)
+def remove_forward(possible_actions: List[str]) -> None:
+    if FORWARD in possible_actions:
+        possible_actions.remove(FORWARD)
 
 
-def result(state: State, action: Action, grid: List[List[str]]) -> State:
+def result(state: State, action: str, grid: List[List[str]]) -> State:
     pass
 
 
@@ -106,7 +105,7 @@ 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
-    return abs(x1 - x2) + abs(y1 - y2)  # Manhattan distance
+    return abs(x1 - x2) + abs(y1 - y2)
 
 
 def f(current_node: Node, goal: Tuple[int, int]) -> int:

logic/level.py

@@ -2,6 +2,7 @@ import random
 
 import pygame
 
+import algorithms.a_star as a_s
 from algorithms.bfs import graphsearch, State
 from common.constants import *
 from common.helpers import castle_neighbors
@@ -110,6 +111,9 @@ class Level:
 
         castle_cords = (self.list_castles[0].position[0], self.list_castles[0].position[1])
         goal_list = castle_neighbors(self.map, castle_cords[0], castle_cords[1])  # list of castle neighbors
+        print(knight_pos_x, knight_pos_y)
+        st = a_s.State((knight_pos_x, knight_pos_y), a_s.UP)
+        print(f'Actions: {a_s.actions(st, self.map)}')
         action_list = graphsearch(state, self.map, goal_list)
         print(action_list)