algorithm - Python - Alpha-Beta Purning for Minimax -


i want implement agent 3-men's morris game-which similar tic-tac-toe game- , want use minimax strategy alpha-beta pruning, here's code in python based on this post , this post on stackoverflow , doesn't work!! gives wrong solution,even when 1 of successors of current state solution

def alpha_beta(state,alpha,beta,turn,depth):   if int(terminal_test(state,turn)) == int(my_number):         return 1  #win   elif (int(terminal_test(state,turn))!=0) , (int(terminal_test(state,turn))!=int(my_number)) :         return -1 #loose   else:     if int(depth) == 13:         return 0  #reached limit    moves = successors(state,turn,int(depth)) #valid moves player based on rules   move in moves:     state = make_move(state,move,turn)     current_eval = -alpha_beta(state, -beta, -alpha, 2-int(turn),int(depth)+1)     state = undo_move(state,move,turn)     if current_eval >= beta:             return beta      if current_eval > alpha:         alpha = current_eval  return alpha   def rootalphabeta(state,depth, turn):     best_move = none     max_eval = float('-infinity')      moves = successors(state,turn,int(depth))     alpha = float('infinity')     move in moves:         state = make_move(state,move,turn)         alpha = -alpha_beta(state, float('-infinity'), alpha, 2-int(turn),int(depth)+1)         state = undo_move(state,move,turn)         if alpha > max_eval:             max_eval = alpha             best_move = move              #best_move selected here not best move!     return best_move


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