Created base game with working minimax algorithm, now working on reinforcement learning

minimax/minimaxAlgo.py

@@ -0,0 +1,56 @@
+import random
+from copy import deepcopy
+from math import inf
+
+from utilities.constants import GREEN, WHITE
+
+
+class MiniMax():
+
+    def AI(self, board, depth, maxPlayer, gameManager):
+        if depth == 0 or board.winner() is not None:
+            return board.scoreOfTheBoard(), board
+
+        if maxPlayer:
+            maxEval = -inf
+            bestMove = None
+            for move in self.getAllMoves(board, maxPlayer):
+                evaluation = self.AI(move, depth - 1, False, gameManager)[0]
+                maxEval = max(maxEval, evaluation)
+                if maxEval > evaluation:
+                    bestMove = move
+                if maxEval == evaluation:
+                    bestMove = bestMove if random.choice([True, False]) else move
+            return maxEval, bestMove
+        else:
+            minEval = inf
+            bestMove = None
+            colour = WHITE if gameManager.turn == GREEN else GREEN
+            for move in self.getAllMoves(board, colour):
+                evaluation = self.AI(move, depth - 1, True, gameManager)[0]
+                minEval = min(minEval, evaluation)
+                if minEval < evaluation:
+                    bestMove = move
+                if minEval == evaluation:
+                    bestMove = bestMove if random.choice([True, False]) else move
+
+            return minEval, bestMove
+
+    def _simulateMove(self, piece, move, board, skip):
+        board.move(piece, move[0], move[1])
+        if skip:
+            board.remove(skip)
+
+        return board
+
+    def getAllMoves(self, board, colour):
+        moves = []
+
+        for piece in board.getAllPieces(colour):
+            validMoves = board.getValidMoves(piece)
+            for move, skip in validMoves.items():
+                tempBoard = deepcopy(board)
+                tempPiece = tempBoard.getPiece(piece.row, piece.col)
+                newBoard = self._simulateMove(tempPiece, move, tempBoard, skip)
+                moves.append(newBoard)
+        return moves