Python A.minimax方法代码示例

# 需要导入模块: import A [as 别名]
# 或者: from A import minimax [as 别名]
 def comp_move(self):
   if self.comp == 'A':
     move = A.minimax(self)
   elif self.comp == 'B':
     vals = {}
     nodes = []
     highest = 0
     best = ()
     moveset = self.valid_moves('W')
     for move in moveset:
       n = tree.Node(self, 3, 'W', move[0], move[1], moveset) # default depth level 3
       nodes.append(n)
     for n in nodes:
       ret = B.alphabeta(n, 3, -10000, 10000, 'W')
       x, y = n.get_x(), n.get_y()
       vals[(x,y)] = ret
       if ret > highest:
         highest = ret
         best = (x,y)
     return best
   else:
     move = C.master(self)
   return move

# 需要导入模块: import A [as 别名]
# 或者: from A import minimax [as 别名]
def master(game):
  """ Given a game and the current board it should go to level depth to determine which move to make. Default depth is 3
  Colour is the colour the computer is playing"""
  def max_val(game, level):
    """ Optimal choice for computer. Returns the move that the computer should make in order to maximise its heuristic value """
    level += 1
    colour = 'W'
    # need to explore deeper nodes
    if level <= depth:
      value = lost # should never be chosen
      best = [0, 0] # just a dummy move
      moves = game.valid_moves(colour).keys()#[1]
      if len(moves)==0: # if we're at a leaf
        best = "pass"
        value = min_val(game, level)
      elif len(moves) == 1:
        best = moves[0]
        moveset = game.valid_moves(colour)#[0]
        new = game.copy()
        new.make_move(moves[0][0],moves[0][1], moveset)
        value = min_val(new, level)
      else:
        for move in moves:
          moveset = game.valid_moves(colour)#[0]
          new = game.copy()
          new.make_move(move[0],move[1], moveset)
          temp = min_val(new, level)
          if temp[0] > value:
            value = temp[0]
            best = move
      return [value, best]
    else:
      value = eval2(game, 'W')
      best = [0,0] # just need a dummy value
      return [value, best] # smallest possible heuristic therefore will never be chosen

  def min_val(game, level):
    """ Optimal choice for opponent. Returns the move that the opponent should make in order to minimise the computers heuristic """
    level += 1
    colour = 'B'
    if level <= depth:
      value = won # will never be chosen as we are looking for the min
      best = [0, 0] # just a dummy move
      moves = game.valid_moves(colour).keys()#[1]
      if len(moves)==0: # if we're at a leaf
        best = "pass"
        value = max_val(game, level)
      elif len(moves) == 1:
        best = moves[0]
        new = game.copy()
        moveset = new.valid_moves(colour)#[0]
        new.make_move(moves[0][0],moves[0][1], moveset)
        value = max_val(new, level)
      else:
        for move in moves:
          new = game.copy()
          moveset = new.valid_moves(colour)#[0]
          new.make_move(move[0],move[1], moveset)
          temp = max_val(new, level) # the optimal move and value that computer will make
          if temp[0] < value: # if we have a smaller heuristic option choose it
            value = temp[0]
            best = move
      return [value, best]
    else:
      value = eval2(game, 'W')
      best = [0,0] # just need a dummy value
      return [value, best] #largest possible heuristic therefore will never be chosen

  def eval1(game, colour):
    if colour=="W":
      return game.board.get_white()
    else:
      return game.board.get_black()

  def eval2(game, colour):
    heur = 0
    # for each token multiply it by stability and add them up
    toks = game.board.get_alltoks()
    for tok in toks:
      if tok.get_colour() == colour:
        heur += tok.get_stability()*2
    # add twice the number of possible moves
    heur += len(new.valid_moves(colour))
    return heur

  # MINIMAX BODY
  # global vars
  level = 1
  depth = 3
  moves = game.valid_moves('W').keys()
  if len(moves) == 0: # no moves possible
    return "pass" # this should never be run because it should be found in premove
  elif len(moves) == 1: # only one possible move
    return moves[0]
  elif len(moves) == 2:
    # choose from best two moves with 20% chance of the worse move
    best = A.minimax(game)
    rand = random.random()
    if rand <= .8:
      return best
#.........这里部分代码省略.........