Merge pull request #1 from PGautam27/master

Added readme and the MiniMaxAlgo

scripts/MiniMaxAlgo/README.md

@@ -0,0 +1,5 @@
+## MiniMax Algorithm
+
+It's implemented in such way that the AI minimizes it's loss and maximizes it's winning chances. 
+
+![minimax](https://user-images.githubusercontent.com/92343715/194568346-bc6c78c3-fe22-43b9-ba25-e2f429b1b5e8.png)

scripts/MiniMaxAlgo/TicTacToe.py

@@ -1,6 +1,6 @@
 import math
 import time
-from player import HumanPlayer, RandomComputerPlayer
+from player import HumanPlayer, SmartComputerPlayer
 
 
 class TicTacToe():

scripts/MiniMaxAlgo/player.py

@@ -27,12 +27,46 @@ class HumanPlayer(Player):
             except ValueError:
                 print('Invalid square. Try again.')
         return val
-
+    
-
+class SmartComputerPlayer(Player):
-class RandomComputerPlayer(Player):
     def __init__(self, letter):
         super().__init__(letter)
 
     def get_move(self, game):
-        square = random.choice(game.available_moves())
+        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