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mcts_pure.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Dec 7 21:19:11 2018
@author: initial-h
"""
import numpy as np
import copy
from operator import itemgetter
from collections import defaultdict
def rollout_policy_fn(board):
'''
a coarse, fast version of policy_fn used in the rollout phase.
'''
action_probs = np.random.rand(len(board.availables)) # rollout randomly
return zip(board.availables, action_probs)
def policy_value_fn(board):
'''
a function that takes in a state and outputs a list of (action, probability)
tuples and a score for the state
'''
# return uniform probabilities and 0 score for pure MCTS
action_probs = np.ones(len(board.availables))/len(board.availables)
return zip(board.availables, action_probs), 0
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 # its the prior probability that action's taken to get this node
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)
# expand all children that under this state
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))
# self._children is a dict
# act_node[1].get_value will return the action with max Q+u and corresponding state
def update(self, leaf_value):
'''
Update node values from leaf evaluation.
leaf_value: the value of subtree evaluation from the current player's perspective.
'''
self._n_visits += 1
# update visit count
self._Q += 1.0*(leaf_value - self._Q) / self._n_visits
# update Q, a running average of values for all visits.
# there is just: (v-Q)/(n+1)+Q = (v-Q+(n+1)*Q)/(n+1)=(v+n*Q)/(n+1)
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)
# every step for revursive update,
# we should change the perspective by the way of taking the negative
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 it's leaf node (i.e. no nodes below this have been expanded).
'''
return self._children == {}
def is_root(self):
'''
check if it's root node
'''
return self._parent is None
class MCTS(object):
'''
A simple implementation of Monte Carlo Tree Search.
'''
def __init__(self, policy_value_fn, c_puct=5, n_playout=400):
'''
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(parent=None, prior_p=1.0)
# root node do not have parent ,and sure with prior probability 1
self._policy = policy_value_fn
self._c_puct = c_puct
self._n_playout = n_playout # times of tree search
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):
# select action in tree
if node.is_leaf():
# break if the node is leaf node
# print('breaking...................................')
break
# Greedily select next move.
action, node = node.select(self._c_puct)
# print('select action is ...',action)
# print(action,state.availables)
state.do_move(action)
# this state should be the same state with current node
action_probs, _ = self._policy(state)
# Check for end of game
end, winner = state.game_end()
if not end:
# expand the node
node.expand(action_probs)
# Evaluate the leaf node by random rollout
leaf_value = self._evaluate_rollout(state)
# Update value and visit count of nodes in this traversal.
node.update_recursive(-leaf_value)
# print('after update...', node._n_visits, node._Q)
def _evaluate_rollout(self, state, limit=1000):
'''
Use the rollout policy to play until the end of the game,
returning +1 if the current player wins, -1 if the opponent wins,
and 0 if it is a tie.
'''
player = state.get_current_player()
for i in range(limit):
end, winner = state.game_end()
if end:
break
action_probs = rollout_policy_fn(state)
max_action = max(action_probs, key=itemgetter(1))[0]
# itemgetter
# https://www.cnblogs.com/zhoufankui/p/6274172.html
state.do_move(max_action)
else:
# If no break from the loop, issue a warning.
print("WARNING: rollout reached move limit")
# print('winner is ...',winner)
if winner == -1: # tie
return 0
else:
return 1 if winner == player else -1
def get_move(self, state):
'''
Runs all playouts sequentially and returns the most visited action.
state: the current game state
Return: the selected action
'''
for n in range(self._n_playout):
state_copy = copy.deepcopy(state)
self._playout(state_copy)
# use deepcopy and playout on the copy state
# some statistics just for check
# visits_count = defaultdict(int)
# visits_count_dic = defaultdict(int)
# self.sum = 0
# Q_U_dic = defaultdict(int)
# for act,node in self._root._children.items():
# visits_count[act] += node._n_visits
# visits_count_dic[str(state.move_to_location(act))] += node._n_visits
# self.sum += node._n_visits
# Q_U_dic[str(state.move_to_location(act))] = node.get_value(5)
# print(Q_U_dic)
# print(self.sum,visits_count_dic)
return max(self._root._children.items(),
key=lambda act_node: act_node[1]._n_visits)[0]
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, c_puct=5, n_playout=400):
'''
init a mcts class
'''
self.mcts = MCTS(policy_value_fn, c_puct, n_playout)
def set_player_ind(self, p):
'''
set player index
'''
self.player = p
def reset_player(self):
'''
reset player
'''
self.mcts.update_with_move(-1) # reset the node
def get_action(self, board,is_selfplay=False,print_probs_value=0):
'''
get an action by mcts
do not discard all the tree and retain the useful part
'''
sensible_moves = board.availables
if board.last_move!=-1:
self.mcts.update_with_move(last_move=board.last_move)
# reuse the tree
# retain the tree that can continue to use
# so update the tree with opponent's move and do mcts from the current node
if len(sensible_moves) > 0:
move = self.mcts.get_move(board)
self.mcts.update_with_move(move)
# every time when get a move, update the tree
else:
print("WARNING: the board is full")
return move, None
def __str__(self):
return "MCTS {}".format(self.player)