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163 lines (130 loc) · 5.72 KB
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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.
'''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:
resig
98C4
n_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_kgs(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)