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# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import sys
import time
from absl import flags
import numpy as np
import coords
import go
import mcts
import sgf_wrapper
flags.DEFINE_integer('softpick_move_cutoff', (go.N * go.N // 12) // 2 * 2,
'The move number (<=) up to which moves are softpicked from MCTS visits.')
# Ensure that both white and black have an equal number of softpicked moves.
flags.register_validator('softpick_move_cutoff', lambda x: x % 2 == 0)
flags.DEFINE_float('resign_threshold', -0.9,
'The post-search Q evaluation at which resign should happen.'
'A threshold of -1 implies resign is disabled.')
flags.register_validator('resign_threshold', lambda x: -1 <= x < 0)
flags.DEFINE_integer('num_readouts', 800,
'Number of searches to add to the MCTS search tree before playing a move.')
flags.register_validator('num_readouts', lambda x: x > 0)
flags.DEFINE_integer('parallel_readouts', 8,
'Number of searches to execute in parallel. This is also the batch size'
'for neural network evaluation.')
FLAGS = flags.FLAGS
def time_recommendation(move_num, seconds_per_move=5, time_limit=15*60,
decay_factor=0.98):
'''Given the current move number and the 'desired' seconds per move, return
how much time should actually be used. This is intended specifically for
CGOS time controls, which has an absolute 15-minute time limit.
The strategy is to spend the maximum possible moves using seconds_per_move,
and then switch to an exponentially decaying time usage, calibrated so that
we have enough time for an infinite number of moves.'''
# Divide by two since you only play half the moves in a game.
player_move_num = move_num / 2
# Sum of geometric series maxes out at endgame_time seconds.
endgame_time = seconds_per_move / (1 - decay_factor)
if endgame_time > time_limit:
# There is so little main time that we're already in 'endgame' mode.
base_time = time_limit * (1 - decay_factor)
core_moves = 0
else:
# Leave over endgame_time seconds for the end, and play at
# seconds_per_move for as long as possible.
base_time = seconds_per_move
core_moves = (time_limit - endgame_time) / seconds_per_move
return base_time * decay_factor ** max(player_move_num - core_moves, 0)
class MCTSPlayerMixin:
def __init__(self, network, seconds_per_move=5, num_readouts=0,
resign_threshold=None, verbosity=0, two_player_mode=False,
timed_match=False):
self.network = network
self.seconds_per_move = seconds_per_move
self.num_readouts = num_readouts or FLAGS.num_readouts
self.verbosity = verbosity
self.two_player_mode = two_player_mode
if two_player_mode:
self.temp_threshold = -1
else:
self.temp_threshold = FLAGS.softpick_move_cutoff
self.qs = []
self.comments = []
self.searches_pi = []
self.root = None
self.result = 0
self.result_string = None
self.resign_threshold = resign_threshold or FLAGS.resign_threshold
self.timed_match = timed_match
assert (self.timed_match and self.seconds_per_move > 0) or self.num_readouts > 0
super().__init__()
def initialize_game(self, position=None):
if position is None:
position = go.Position()
self.root = mcts.MCTSNode(position)
self.result = 0
self.result_string = None
self.comments = []
self.searches_pi = []
self.qs = []
def suggest_move(self, position):
''' Used for playing a single game.
For parallel play, use initialize_move, select_leaf,
incorporate_results, and pick_move
'''
start = time.time()
if self.timed_match:
while time.time() - start < self.seconds_per_move:
self.tree_search()
else:
current_readouts = self.root.N
while self.root.N < current_readouts + self.num_readouts:
self.tree_search()
if self.verbosity > 0:
print("%d: Searched %d times in %s seconds\n\n" % (
position.n, self.num_readouts, time.time() - start), file=sys.stderr)
# print some stats on anything with probability > 1%
if self.verbosity > 2:
print(self.root.describe(), file=sys.stderr)
print('\n\n', file=sys.stderr)
if self.verbosity > 3:
print(self.root.position, file=sys.stderr)
return self.pick_move()
def play_move(self, c):
'''
Notable side effects:
- finalizes the probability distribution according to
this roots visit counts into the class' running tally, `searches_pi`
- Makes the node associated with this move the root, for future
`inject_noise` calls.
'''
if not self.two_player_mode:
self.searches_pi.append(
self.root.children_as_pi(self.root.position.n <= self.temp_threshold))
self.qs.append(self.root.Q) # Save our resulting Q.
self.comments.append(self.root.describe())
try:
self.root = self.root.maybe_add_child(coords.to_flat(c))
except go.IllegalMove:
print("Illegal move")
if not self.two_player_mode:
self.searches_pi.pop()
self.qs.pop()
self.comments.pop()
return False
self.position = self.root.position # for showboard
del self.root.parent.children
return True # GTP requires positive result.
def pick_move(self):
'''Picks a move to play, based on MCTS readout statistics.
Highest N is most robust indicator. In the early stage of the game, pick
a move weighted by visit count; later on, pick the absolute max.'''
if self.root.position.n >= self.temp_threshold:
fcoord = np.argmax(self.root.child_N)
else:
cdf = self.root.child_N.cumsum()
cdf /= cdf[-2] # Prevents passing via softpick.
selection = random.random()
fcoord = cdf.searchsorted(selection)
assert self.root.child_N[fcoord] != 0
return coords.from_flat(fcoord)
def tree_search(self, parallel_readouts=None):
if parallel_readouts is None:
parallel_readouts = FLAGS.parallel_readouts
leaves = []
failsafe = 0
while len(leaves) < parallel_readouts and failsafe < parallel_readouts * 2:
failsafe += 1
leaf = self.root.select_leaf()
if self.verbosity >= 4:
print(self.show_path_to_root(leaf))
# if game is over, override the value estimate with the true score
if leaf.is_done():
value = 1 if leaf.position.score() > 0 else -1
leaf.backup_value(value, up_to=self.root)
continue
leaf.add_virtual_loss(up_to=self.root)
leaves.append(leaf)
if leaves:
move_probs, values = self.network.run_many(
[leaf.position for leaf in leaves])
for leaf, move_prob, value in zip(leaves, move_probs, values):
leaf.revert_virtual_loss(up_to=self.root)
leaf.incorporate_results(move_prob, value, up_to=self.root)
return leaves
def show_path_to_root(self, node):
pos = node.position
diff = node.position.n - self.root.position.n
if len(pos.recent) == 0:
return
def fmt(move): return "{}-{}".format('b' if move.color == 1 else 'w',
coords.to_kgs(move.move))
path = " ".join(fmt(move) for move in pos.recent[-diff:])
if node.position.n >= FLAGS.max_game_length:
path += " (depth cutoff reached) %0.1f" % node.position.score()
elif node.position.is_game_over():
path += " (game over) %0.1f" % node.position.score()
return path
def is_done(self):
return self.result != 0 or self.root.is_done()
def should_resign(self):
'''Returns true if the player resigned. No further moves should be played'''
return self.root.Q_perspective < self.resign_threshold
def set_result(self, winner, was_resign):
self.result = winner
if was_resign:
string = "B+R" if winner == go.BLACK else "W+R"
else:
string = self.root.position.result_string()
self.result_string = string
def to_sgf(self, use_comments=True):
assert self.result_string is not None
pos = self.root.position
if use_comments:
comments = self.comments or ['No comments.']
comments[0] = ("Resign Threshold: %0.3f\n" %
self.resign_threshold) + comments[0]
else:
comments = []
return sgf_wrapper.make_sgf(pos.recent, self.result_string,
white_name=os.path.basename(
self.network.save_file) or "Unknown",
black_name=os.path.basename(
self.network.save_file) or "Unknown",
comments=comments)
def extract_data(self):
assert len(self.searches_pi) == self.root.position.n
assert self.result != 0
for pwc, pi in zip(go.replay_position(self.root.position, self.result),
self.searches_pi):
yield pwc.position, pi, pwc.result
def chat(self, msg_type, sender, text):
default_response = "Supported commands are 'winrate', 'nextplay', 'fortune', and 'help'."
if self.root is None or self.root.position.n == 0:
return "I'm not playing right now. " + default_response
if 'winrate' in text.lower():
wr = (abs(self.root.Q) + 1.0) / 2.0
color = "Black" if self.root.Q > 0 else "White"
return "{:s} {:.2f}%".format(color, wr * 100.0)
elif 'nextplay' in text.lower():
return "I'm thinking... " + self.root.most_visited_path()
elif 'fortune' in text.lower():
return "You're feeling lucky!"
elif 'help' in text.lower():
return "I can't help much with go -- try ladders! Otherwise: " + default_response
else:
return default_response
class CGOSPlayerMixin(MCTSPlayerMixin):
def suggest_move(self, position):
self.seconds_per_move = time_recommendation(position.n)
return super().suggest_move(position)