Repository navigation
Expand file tree
/
Copy pathselfplay.py
More file actions
164 lines (131 loc) · 5.73 KB
/
Copy pathselfplay.py
File metadata and controls
164 lines (131 loc) · 5.73 KB
1
2
3
4
5
6
7
8
9
10
<
8000
div data-line-number="11" class="react-line-number react-code-text" style="padding-right:16px">1112
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
# 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.
"""Python implementation of selfplay worker.
This worker is used to set up many parallel selfplay instances."""
import random
import os
import socket
import time
from absl import app, flags
from tensorflow import gfile
import coords
import dual_net
import preprocessing
from strategies import MCTSPlayer
import utils
flags.DEFINE_string('load_file', None, 'Path to model save files.')
flags.DEFINE_string('selfplay_dir', None, 'Where to write game data.')
flags.DEFINE_string('holdout_dir', None, 'Where to write held-out game data.')
flags.DEFINE_string('sgf_dir', None, 'Where to write human-readable SGFs.')
flags.DEFINE_float('holdout_pct', 0.05, 'What percent of games to hold out.')
flags.DEFINE_float('resign_disable_pct', 0.05,
'What percent of games to disable resign for.')
# From strategies.py
flags.declare_key_flag('verbose')
flags.declare_key_flag('num_readouts')
FLAGS = flags.FLAGS
def play(network):
"""Plays out a self-play match, returning a MCTSPlayer object containing:
- the final position
- the n x 362 tensor of floats representing the mcts search probabilities
- the n-ary tensor of floats representing the original value-net estimate
where n is the number of moves in the game
"""
readouts = FLAGS.num_readouts # defined in strategies.py
# Disable resign in 5% of games
if random.random() < FLAGS.resign_disable_pct:
resign_threshold = -1.0
else:
resign_threshold = None
player = MCTSPlayer(network, resign_threshold=resign_threshold)
player.initialize_game()
# Must run this once at the start to expand the root node.
first_node = player.root.select_leaf()
prob, val = network.run(first_node.position)
first_node.incorporate_results(prob, val, first_node)
while True:
start = time.time()
player.root.inject_noise()
current_readouts = player.root.N
# we want to do "X additional readouts", rather than "up to X readouts".
while player.root.N < current_readouts + readouts:
player.tree_search()
if FLAGS.verbose >= 3:
print(player.root.position)
print(player.root.describe())
if player.should_resign():
player.set_result(-1 * player.root.position.to_play,
was_resign=True)
break
move = player.pick_move()
player.play_move(move)
if player.root.is_done():
player.set_result(player.root.position.result(), was_resign=False)
break
if (FLAGS.verbose >= 2) or (FLAGS.verbose >= 1 and player.root.position.n % 10 == 9):
print("Q: {:.5f}".format(player.root.Q))
dur = time.time() - start
print("%d: %d readouts, %.3f s/100. (%.2f sec)" % (
player.root.position.n, readouts, dur / readouts * 100.0, dur), flush=True)
if FLAGS.verbose >= 3:
print("Played >>",
coords.to_gtp(coords.from_flat(player.root.fmove)))
if FLAGS.verbose >= 2:
utils.dbg("%s: %.3f" % (player.result_string, player.root.Q))
utils.dbg(player.root.position, player.root.position.score())
return player
def run_game(load_file, selfplay_dir=None, holdout_dir=None,
sgf_dir=None, holdout_pct=0.05):
"""Takes a played game and record results and game data."""
if sgf_dir is not None:
minimal_sgf_dir = os.path.join(sgf_dir, 'clean')
full_sgf_dir = os.path.join(sgf_dir, 'full')
utils.ensure_dir_exists(minimal_sgf_dir)
utils.ensure_dir_exists(full_sgf_dir)
if selfplay_dir is not None:
utils.ensure_dir_exists(selfplay_dir)
utils.ensure_dir_exists(holdout_dir)
with utils.logged_timer("Loading weights from %s ... " % load_file):
network = dual_net.DualNetwork(load_file)
with utils.logged_timer("Playing game"):
player = play(network)
output_name = '{}-{}'.format(int(time.time()), socket.gethostname())
game_data = player.extract_data()
if sgf_dir is not None:
with gfile.GFile(os.path.join(minimal_sgf_dir, '{}.sgf'.format(output_name)), 'w') as f:
f.write(player.to_sgf(use_comments=False))
with gfile.GFile(os.path.join(full_sgf_dir, '{}.sgf'.format(output_name)), 'w') as f:
f.write(player.to_sgf())
tf_examples = preprocessing.make_dataset_from_selfplay(game_data)
if selfplay_dir is not None:
# Hold out 5% of games for validation.
if random.random() < holdout_pct:
fname = os.path.join(holdout_dir,
"{}.tfrecord.zz".format(output_name))
else:
fname = os.path.join(selfplay_dir,
"{}.tfrecord.zz".format(output_name))
preprocessing.write_tf_examples(fname, tf_examples)
def main(argv):
"""Entry point for running one selfplay game."""
del argv # Unused
flags.mark_flag_as_required('load_file')
run_game(
load_file=FLAGS.load_file,
selfplay_dir=FLAGS.selfplay_dir,
holdout_dir=FLAGS.holdout_dir,
holdout_pct=FLAGS.holdout_pct,
sgf_dir=FLAGS.sgf_dir)
if __name__ == '__main__':
app.run(main)