From fae51028b1f4f3b1b528a4e515f3b24b63873134 Mon Sep 17 00:00:00 2001
From: P Gautam <92343715+PGautam27@users.noreply.github.com>
Date: Fri, 7 Oct 2022 19:09:56 +0530
Subject: [PATCH] Added miniMax
---
scripts/MiniMaxAlgo/player.py | 43 +++++++++++++++++++++++++++++++++++
1 file changed, 43 insertions(+)
diff --git a/scripts/MiniMaxAlgo/player.py b/scripts/MiniMaxAlgo/player.py
index 2ffe8cc..34e997c 100644
--- a/scripts/MiniMaxAlgo/player.py
+++ b/scripts/MiniMaxAlgo/player.py
@@ -36,3 +36,46 @@ class RandomComputerPlayer(Player):
def get_move(self, game):
square = random.choice(game.available_moves())
return square
+
+class SmartComputerPlayer(Player):
+ def __init__(self, letter):
+ super().__init__(letter)
+
+ def get_move(self, game):
+ if len(game.available_moves()) == 9:
+ square = random.choice(game.available_moves())
+ else:
+ square = self.minimax(game, self.letter)['position']
+ return square
+
+ def minimax(self, state, player):
+ max_player = self.letter # yourself
+ other_player = 'O' if player == 'X' else 'X'
+
+ # first we want to check if the previous move is a winner
+ if state.current_winner == other_player:
+ return {'position': None, 'score': 1 * (state.num_empty_squares() + 1) if other_player == max_player else -1 * (
+ state.num_empty_squares() + 1)}
+ elif not state.empty_squares():
+ return {'position': None, 'score': 0}
+
+ if player == max_player:
+ best = {'position': None, 'score': -math.inf} # each score should maximize
+ else:
+ best = {'position': None, 'score': math.inf} # each score should minimize
+ for possible_move in state.available_moves():
+ state.make_move(possible_move, player)
+ sim_score = self.minimax(state, other_player) # simulate a game after making that move
+
+ # undo move
+ state.board[possible_move] = ' '
+ state.current_winner = None
+ sim_score['position'] = possible_move # this represents the move optimal next move
+
+ if player == max_player: # X is max player
+ if sim_score['score'] > best['score']:
+ best = sim_score
+ else:
+ if sim_score['score'] < best['score']:
+ best = sim_score
+ return best