text: # -*- coding: utf-8 -*-
  """
  Monte Carlo Tree Search in AlphaGo Zero style, which uses a policy-value
  network to guide the tree search and evaluate the leaf nodes
  
  @author: Junxiao Song
  """
  
  import numpy as np
  import copy
  from Forbidden_Moves import forbidden_move
  
  def softmax(x):
      probs = np.exp(x - np.max(x))
      probs /= np.sum(probs)
      return probs
  
  
  class TreeNode(object):
      """A node in the MCTS tree.
  
      Each node keeps track of its own value Q, prior probability P, and
      its visit-count-adjusted prior score u.
      """
  
      def __init__(self, parent, prior_p):
          self._parent = parent
          self._children = {}  # a map from action to TreeNode
          self._n_visits = 0
          self._Q = 0
          self._u = 0
          self._P = prior_p
  
      def expand(self, action_priors):
          """Expand tree by creating new children.
          action_priors: a list of tuples of actions and their prior probability
              according to the policy function.
          """
          for action, prob in action_priors:
              if action not in self._children:
                  self._children[action] = TreeNode(self, prob)
  
      def select(self, c_puct):
          """Select action among children that gives maximum action value Q
          plus bonus u(P).
          Return: A tuple of (action, next_node)
          """
          # print(self._children)
          # print(self._children.items()[1].get_value(c_puct))
          return max(self._children.items(),
                     key=lambda act_node: act_node[1].get_value(c_puct))
  
      def update(self, leaf_value):
          """Update node values from leaf evaluation.
          leaf_value: the value of subtree evaluation from the current player's
              perspective.
          """
          # Count visit.
          self._n_visits += 1
          # Update Q, a running average of values for all visits.
          self._Q += 1.0*(leaf_value - self._Q) / self._n_visits
  
      def update_recursive(self, leaf_value):
          """Like a call to update(), but applied recursively for all ancestors.
          """
          # If it is not root, this node's parent should be updated first.
          if self._parent:
              self._parent.update_recursive(-leaf_value)
          self.update(leaf_value)
  
      def get_value(self, c_puct):
          """Calculate and return the value for this node.
          It is a combination of leaf evaluations Q, and this node's prior
          adjusted for its visit count, u.
          c_puct: a number in (0, inf) controlling the relative impact of
              value Q, and prior probability P, on this node's score.
          """
          self._u = (c_puct * self._P *
                     np.sqrt(self._parent._n_visits) / (1 + self._n_visits))
          return self._Q + self._u
  
      def is_leaf(self):
          """Check if leaf node (i.e. no nodes below this have been expanded)."""
          return self._children == {}
  
      def is_root(self):
          return self._parent is None
  
  
  class MCTS(object):
      """An implementation of Monte Carlo Tree Search."""
  
      def __init__(self, policy_value_fn, c_puct=5, n_playout=10000):
          """
          policy_value_fn: a function that takes in a board state and outputs
              a list of (action, probability) tuples and also a score in [-1, 1]
              (i.e. the expected value of the end game score from the current
              player's perspective) for the current player.
          c_puct: a number in (0, inf) that controls how quickly exploration
              converges to the maximum-value policy. A higher value means
              relying on the prior more.
          """
          self._root = TreeNode(None, 1.0)
          self._policy = policy_value_fn
          self._c_puct = c_puct
          self._n_playout = n_playout
  
      def _playout(self, state):
          """Run a single playout from the root to the leaf, getting a value at
          the leaf and propagating it back through its parents.
          State is modified in-place, so a copy must be provided.
          """
          node = self._root
          while(1):
              if node.is_leaf():
                  break
              # Greedily select next move.
              action, node = node.select(self._c_puct)
              state.do_move(action)
  
          # Evaluate the leaf using a network which outputs a list of
          # (action, probability) tuples p and also a score v in [-1, 1]
          # for the current player.
          action_probs, leaf_value = self._policy(state)
          # Check for end of game.
          end, winner = state.game_end()
          if not end:
              node.expand(action_probs)
          else:
              # for end state，return the "true" leaf_value
              if winner == -1:  # tie
                  leaf_value = 0.0
              else:
                  leaf_value = (
                      1.0 if winner == state.get_current_player() else -1.0
                  )
  
          # Update value and visit count of nodes in this traversal.
          node.update_recursive(-leaf_value)
  
      def get_move_probs(self, state, temp=1e-3):
          """Run all playouts sequentially and return the available actions and
          their corresponding probabilities.
          state: the current game state
          temp: temperature parameter in (0, 1] controls the level of exploration
          """
          for n in range(self._n_playout):
              state_copy = copy.deepcopy(state)
              self._playout(state_copy)
  
          # calc the move probabilities based on visit counts at the root node
          act_visits = [(act, node._n_visits)
                        for act, node in self._root._children.items()]
          acts, visits = zip(*act_visits)
          act_probs = softmax(1.0/temp * np.log(np.array(visits) + 1e-10))
  
          return acts, act_probs
  
      def update_with_move(self, last_move):
          """Step forward in the tree, keeping everything we already know
          about the subtree.
          """
          if last_move in self._root._children:
              self._root = self._root._children[last_move]
              self._root._parent = None
          else:
              self._root = TreeNode(None, 1.0)
  
      def __str__(self):
          return "MCTS"
  
  
  class MCTSPlayer(object):
      """AI player based on MCTS"""
  
      def __init__(self, policy_value_function,
                   c_puct=5, n_playout=2000, is_selfplay=0):
          self.mcts = MCTS(policy_value_function, c_puct, n_playout)
          self._is_selfplay = is_selfplay
  
      def set_player_ind(self, p):
          self.player = p
  
      def reset_player(self):
          self.mcts.update_with_move(-1)
  
      def get_action(self, board, temp=1e-3, return_prob=0):
          # sensible_moves = board.availables
          sensible_moves=np.copy(board.availables)
          # print('board.current_player',board.current_player)# should==2:
  
          # for m in board.save_forbidden:
          #     if m in sensible_moves:
          #         print(m)
          #         print(board.move_to_location(m))
          #         list(sensible_moves).remove(m)
          # board.save_forbidden=
  
          # the pi vector returned by MCTS as in the alphaGo Zero paper
          move_probs = np.zeros(board.width*board.height)
          if len(sensible_moves) > 0:
              acts, probs = self.mcts.get_move_probs(board, temp)
              # print(probs)
              # print(len(probs))
              # for m in board.save_forbidden:
              #     probs[m]=0
              # move_probs[list(acts)] = probs
              if self._is_selfplay:
                  # add Dirichlet Noise for exploration (needed for
                  # self-play training)
                  move = np.random.choice(
                      acts,
                      p=0.75*probs + 0.25*np.random.dirichlet(0.3*np.ones(len(probs)))
                  )
                  # print(move)
                  # update the root node and reuse the search tree
                  self.mcts.update_with_move(move)
              else:
                  # with the default temp=1e-3, it is almost equivalent
                  # to choosing the move with the highest prob
                  move = np.random.choice(acts, p=probs)
                  # while move in board.save_forbidden:
                      # move = np.random.choice(acts, p=probs)
                  # print('move',move)
                  # print('probs',probs)
                  # print('probs[35]',probs[35])
                  # print(np.where(probs==1))
                  # reset the root node
                  self.mcts.update_with_move(-1)
  #                location = board.move_to_location(move)
  #                print("AI move: %d,%d\n" % (location[0], location[1]))
              if return_prob:
                  return move, move_probs
              else:
                  return move
          else:
              print("WARNING: the board is full")
  
      def __str__(self):
          return "MCTS {}".format(self.player)
  