def play(network, readouts, resign_threshold, verbosity=0):
''' Plays out a self-play match, returning
- 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'''
player = MCTSPlayer(network,
resign_threshold=resign_threshold,
verbosity=verbosity,
num_parallel=SIMULTANEOUS_LEAVES)
global_n = 0
# Disable resign in 5% of games
if random.random() < 0.05:
player.resign_threshold = -0.9999
player.initialize_game()
# Must run this once at the start, so that noise injection actually
# affects the first move of the game.
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 (verbosity >= 3):
print(player.root.position)
print(player.root.describe())
# Sets is_done to be True if player.should resign.
if player.should_resign(): # TODO: make this less side-effecty.
break
move = player.pick_move()
player.play_move(move)
if player.is_done():
# TODO: actually handle the result instead of ferrying it around as a property.
player.result = player.position.result()
break
if (verbosity >= 2) or (verbosity >= 1 and player.root.position.n % 10 == 9):
print("Q: {}".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 verbosity >= 3:
print("Played >>",
coords.to_human_coord(coords.unflatten_coords(player.root.fmove)))
# TODO: break when i >= 2 * go.N * go.N (where is this being done now??...)
return player
def predict_move(filename, network):
# Strategies: def initialize_game(self, position=None):
#filename = '/usr/local/google/home/vbittorf/projects/minigo/benchmark_sgf/prob_0001.sgf'
replay = []
with open(filename) as f:
text = f.read()
print(text)
for position_w_context in sgf_wrapper.replay_sgf(text):
replay.append(position_w_context)
print(replay)
# model_path = '/usr/local/google/home/vbittorf/Documents/minigo/rl_pipeline/models/000003-leopard'
#model_path = '/usr/local/google/home/vbittorf/Documents/minigo/20hour1000game/models/000002-nassau'
#white_net = dual_net.DualNetwork(model_path)
#black_net = dual_net.DualNetwork(model_path)
#print(evaluation.play_match(white_net, black_net, 1, 50, "/tmp/sgf", 0))
black_net = network
player = MCTSPlayer(black_net,
verbosity=0,
two_player_mode=True,
num_parallel=4)
readouts = 361 * 10
tried = 0
correct = 0
for position_w_context in replay:
if position_w_context.next_move is None:
continue
player.initialize_game(position_w_context.position)
current_readouts = player.root.N
while player.root.N < current_readouts + readouts:
player.tree_search()
move = player.pick_move()
# if player.should_resign(): # Force resign
# move = 'R'
# else:
# move = player.suggest_move(position_w_context.position)
tried += 1
if move == position_w_context.next_move:
correct += 1
player.play_move(move)
print(player.root.position)
print(move, position_w_context.next_move)
return move, position_w_context.next_move, move == position_w_context.next_move
print('Correct: ', correct * 1.0 / tried)
def predict_move(filename, network):
# Strategies: def initialize_game(self, position=None):
#filename = '/usr/local/google/home/vbittorf/projects/minigo/benchmark_sgf/prob_0001.sgf'
replay = []
with open(filename) as f:
text = f.read()
print(text)
for position_w_context in sgf_wrapper.replay_sgf(text):
replay.append(position_w_context)
print(replay)
# model_path = '/usr/local/google/home/vbittorf/Documents/minigo/rl_pipeline/models/000003-leopard'
#model_path = '/usr/local/google/home/vbittorf/Documents/minigo/20hour1000game/models/000002-nassau'
#white_net = dual_net.DualNetwork(model_path)
#black_net = dual_net.DualNetwork(model_path)
#print(evaluation.play_match(white_net, black_net, 1, 50, "/tmp/sgf", 0))
black_net = network
player = MCTSPlayer(
black_net, verbosity=0, two_player_mode=True, num_parallel=4)
readouts = 361 * 10
tried = 0
correct = 0
for position_w_context in replay:
if position_w_context.next_move is None:
continue
player.initialize_game(position_w_context.position)
current_readouts = player.root.N
while player.root.N < current_readouts + readouts:
player.tree_search()
move = player.pick_move()
#if player.should_resign(): # Force resign
# move = 'R'
#else:
# move = player.suggest_move(position_w_context.position)
tried += 1
if move == position_w_context.next_move:
correct += 1
player.play_move(move)
print(player.root.position)
print(move, position_w_context.next_move)
return move, position_w_context.next_move, move == position_w_context.next_move
print('Correct: ', correct * 1.0 / tried)
def play(network, readouts, resign_threshold, verbosity=0):
''' Plays out a self-play match, returning
- 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'''
player = MCTSPlayer(network,
resign_threshold=resign_threshold,
verbosity=verbosity,
num_parallel=SIMULTANEOUS_LEAVES)
global_n = 0
# Disable resign in 5% of games
if random.random() < 0.05:
player.resign_threshold = -1.0
player.initialize_game()
# Must run this once at the start, so that noise injection actually
# affects the first move of the game.
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 (verbosity >= 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 (verbosity >= 2) or (verbosity >= 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 verbosity >= 3:
print("Played >>",
coords.to_human_coord(coords.unflatten_coords(player.root.fmove)))
if verbosity >= 2:
print("%s: %.3f" % (player.result_string, player.root.Q), file=sys.stderr)
print(player.root.position,
player.root.position.score(), file=sys.stderr)
return player
def play(board_size, network, readouts, resign_threshold, simultaneous_leaves,
verbosity=0):
"""Plays out a self-play match.
Args:
board_size: the go board size
network: the DualNet model
readouts: the number of readouts in MCTS
resign_threshold: the threshold to resign at in the match
simultaneous_leaves: the number of simultaneous leaves in MCTS
verbosity: the verbosity of the self-play match
Returns:
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.
"""
player = MCTSPlayer(board_size, network, resign_threshold=resign_threshold,
verbosity=verbosity, num_parallel=simultaneous_leaves)
# Disable resign in 5% of games
if random.random() < 0.05:
player.resign_threshold = -1.0
player.initialize_game()
# Must run this once at the start, so that noise injection actually
# affects the first move of the game.
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 verbosity >= 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 (verbosity >= 2) or (
verbosity >= 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))
if verbosity >= 3:
print("Played >>",
coords.to_kgs(coords.from_flat(player.root.fmove)))
if verbosity >= 2:
print("%s: %.3f" % (player.result_string, player.root.Q), file=sys.stderr)
print(player.root.position,
player.root.position.score(), file=sys.stderr)
return player
def play(board_size,
network,
readouts,
resign_threshold,
simultaneous_leaves,
verbosity=0):
"""Plays out a self-play match.
Args:
board_size: the go board size
network: the DualNet model
readouts: the number of readouts in MCTS
resign_threshold: the threshold to resign at in the match
simultaneous_leaves: the number of simultaneous leaves in MCTS
verbosity: the verbosity of the self-play match
Returns:
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.
"""
player = MCTSPlayer(board_size,
network,
resign_threshold=resign_threshold,
verbosity=verbosity,
num_parallel=simultaneous_leaves)
# Disable resign in 5% of games
if random.random() < 0.05:
player.resign_threshold = -1.0
player.initialize_game()
# Must run this once at the start, so that noise injection actually
# affects the first move of the game.
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 verbosity >= 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 (verbosity >= 2) or (verbosity >= 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))
if verbosity >= 3:
print("Played >>",
coords.to_kgs(coords.from_flat(player.root.fmove)))
if verbosity >= 2:
print("%s: %.3f" % (player.result_string, player.root.Q),
file=sys.stderr)
print(player.root.position,
player.root.position.score(),
file=sys.stderr)
return player