Implemented expectimax algorithm

expectimax.py

+from minmax import MinMax
+
+
+class Expectimax(MinMax):
+    """
+    Initializes an Expectimax object.
+
+    :param maze: The maze used for navigation and solving.
+    """
+    def __init__(self, maze):
+        super().__init__(maze)
+
+    """
+    Performs the Expectimax algorithm to determine the best move the enemy can take.
+
+    :param player_position: The current position of the player agent.
+    :param enemy_position: The current position of the enemy agent.
+    :param depth: The depth of the Expectimax search tree.
+    :param maximising_player: True if maximising player (enemy), False if minimising player (player).
+    :return: The best move for the enemy agent.
+    """
+    def expectimax(self, player_position, enemy_position, depth, maximising_player):
+        if depth == 0 or self.game_over():
+            # Evaluate the current game state
+            return self.evaluate(player_position, enemy_position)
+
+        if maximising_player:
+            max_eval = float('-inf')
+            valid_moves = self.get_valid_moves(enemy_position)
+            for move in valid_moves:
+                if self.is_valid_move(move):
+                    eval = self.expectimax(player_position, move, depth - 1, False)
+                    max_eval = max(max_eval, eval)
+            return max_eval
+        else:
+            total_eval = 0
+            valid_moves = self.get_valid_moves(player_position)
+            num_moves = len(valid_moves)
+            for move in valid_moves:
+                if self.is_valid_move(move):
+                    eval = self.expectimax(move, enemy_position, depth - 1, True)
+                    total_eval += eval
+            return total_eval / num_moves
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