# -*- 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 import time from concurrent.futures import ThreadPoolExecutor import threading 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) """ 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, lock=None): """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 if lock is not None: lock.acquire() while(1): if node.is_leaf(): break # Greedily select next move. action, node = node.select(self._c_puct) state.do_move(action) if lock is not None: lock.release() # 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 lock is not None: lock.acquire() if not end: node.expand(action_probs) else: # for end stateļ¼Œreturn 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) if lock is not None: lock.release() 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 """ start_time_averge = 0 ### test multi-thread # lock = threading.Lock() # with ThreadPoolExecutor(max_workers=4) as executor: # for n in range(self._n_playout): # start_time = time.time() # state_copy = copy.deepcopy(state) # executor.submit(self._playout, state_copy, lock) # start_time_averge += (time.time() - start_time) ### end test multi-thread t = time.time() for n in range(self._n_playout): start_time = time.time() state_copy = copy.deepcopy(state) self._playout(state_copy) start_time_averge += (time.time() - start_time) total_time = time.time() - t # print('!!time!!:', time.time() - t) print(f" My MCTS sum_time: {total_time }, total_simulation: {self._n_playout}") # 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 0, acts, act_probs, total_time 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,return_time = False): sensible_moves = board.availables # 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, simul_mean_time = self.mcts.get_move_probs(board, temp) 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))) ) # 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) # 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_time: if return_prob: return move, move_probs,simul_mean_time else: return move,simul_mean_time else: 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)