def test_add_child(self):
root = MCTSNode(utils_test.BOARD_SIZE,
go.Position(utils_test.BOARD_SIZE))
child = root.maybe_add_child(17)
self.assertIn(17, root.children)
self.assertEqual(child.parent, root)
self.assertEqual(child.fmove, 17)
def test_add_child_idempotency(self):
root = MCTSNode(utils_test.BOARD_SIZE,
go.Position(utils_test.BOARD_SIZE))
child = root.maybe_add_child(17)
current_children = copy.copy(root.children)
child2 = root.maybe_add_child(17)
self.assertEqual(child, child2)
self.assertEqual(current_children, root.children)
def initialize_game(self, position=None):
if position is None:
position = go.Position(self.board_size)
self.root = MCTSNode(self.board_size, position)
self.result = 0
self.result_string = None
self.comments = []
self.searches_pi = []
self.qs = []
def test_select_leaf(self):
flattened = coords.to_flat(
utils_test.BOARD_SIZE, coords.from_kgs(utils_test.BOARD_SIZE,
'D9'))
probs = np.array([.02] *
(utils_test.BOARD_SIZE * utils_test.BOARD_SIZE + 1))
probs[flattened] = 0.4
root = MCTSNode(utils_test.BOARD_SIZE, SEND_TWO_RETURN_ONE)
root.select_leaf().incorporate_results(probs, 0, root)
self.assertEqual(root.position.to_play, go.WHITE)
self.assertEqual(root.select_leaf(), root.children[flattened])
def test_do_not_explore_past_finish(self):
probs = np.array([0.02] *
(utils_test.BOARD_SIZE * utils_test.BOARD_SIZE + 1),
dtype=np.float32)
root = MCTSNode(utils_test.BOARD_SIZE,
go.Position(utils_test.BOARD_SIZE))
root.select_leaf().incorporate_results(probs, 0, root)
first_pass = root.maybe_add_child(
coords.to_flat(utils_test.BOARD_SIZE, None))
first_pass.incorporate_results(probs, 0, root)
second_pass = first_pass.maybe_add_child(
coords.to_flat(utils_test.BOARD_SIZE, None))
with self.assertRaises(AssertionError):
second_pass.incorporate_results(probs, 0, root)
node_to_explore = second_pass.select_leaf()
# should just stop exploring at the end position.
self.assertEqual(node_to_explore, second_pass)
def test_action_flipping(self):
np.random.seed(1)
probs = np.array([.02] *
(utils_test.BOARD_SIZE * utils_test.BOARD_SIZE + 1))
probs += np.random.random(
[utils_test.BOARD_SIZE * utils_test.BOARD_SIZE + 1]) * 0.001
black_root = MCTSNode(utils_test.BOARD_SIZE,
go.Position(utils_test.BOARD_SIZE))
white_root = MCTSNode(
utils_test.BOARD_SIZE,
go.Position(utils_test.BOARD_SIZE, to_play=go.WHITE))
black_root.select_leaf().incorporate_results(probs, 0, black_root)
white_root.select_leaf().incorporate_results(probs, 0, white_root)
# No matter who is to play, when we know nothing else, the priors
# should be respected, and the same move should be picked
black_leaf = black_root.select_leaf()
white_leaf = white_root.select_leaf()
self.assertEqual(black_leaf.fmove, white_leaf.fmove)
self.assertEqualNPArray(black_root.child_action_score,
white_root.child_action_score)
def test_never_select_illegal_moves(self):
probs = np.array([0.02] *
(utils_test.BOARD_SIZE * utils_test.BOARD_SIZE + 1))
# let's say the NN were to accidentally put a high weight on an illegal move
probs[1] = 0.99
root = MCTSNode(utils_test.BOARD_SIZE, SEND_TWO_RETURN_ONE)
root.incorporate_results(probs, 0, root)
# and let's say the root were visited a lot of times, which pumps up the
# action score for unvisited moves...
root.N = 100000
root.child_N[root.position.all_legal_moves()] = 10000
# this should not throw an error...
leaf = root.select_leaf()
# the returned leaf should not be the illegal move
self.assertNotEqual(leaf.fmove, 1)
# and even after injecting noise, we should still not select an illegal move
for _ in range(10):
root.inject_noise()
leaf = root.select_leaf()
self.assertNotEqual(leaf.fmove, 1)
def test_dont_pick_unexpanded_child(self):
probs = np.array([0.001] *
(utils_test.BOARD_SIZE * utils_test.BOARD_SIZE + 1))
# make one move really likely so that tree search goes down that path twice
# even with a virtual loss
probs[17] = 0.999
root = MCTSNode(utils_test.BOARD_SIZE,
go.Position(utils_test.BOARD_SIZE))
root.incorporate_results(probs, 0, root)
leaf1 = root.select_leaf()
self.assertEqual(leaf1.fmove, 17)
leaf1.add_virtual_loss(up_to=root)
# the second select_leaf pick should return the same thing, since the child
# hasn't yet been sent to neural net for eval + result incorporation
leaf2 = root.select_leaf()
self.assertIs(leaf1, leaf2)
def test_backup_incorporate_results(self):
probs = np.array([.02] *
(utils_test.BOARD_SIZE * utils_test.BOARD_SIZE + 1))
root = MCTSNode(utils_test.BOARD_SIZE, SEND_TWO_RETURN_ONE)
root.select_leaf().incorporate_results(probs, 0, root)
leaf = root.select_leaf()
leaf.incorporate_results(probs, -1, root) # white wins!
# Root was visited twice: first at the root, then at this child.
self.assertEqual(root.N, 2)
# Root has 0 as a prior and two visits with value 0, -1
self.assertAlmostEqual(root.Q, -1 / 3) # average of 0, 0, -1
# Leaf should have one visit
self.assertEqual(root.child_N[leaf.fmove], 1)
self.assertEqual(leaf.N, 1)
# And that leaf's value had its parent's Q (0) as a prior, so the Q
# should now be the average of 0, -1
self.assertAlmostEqual(root.child_Q[leaf.fmove], -0.5)
self.assertAlmostEqual(leaf.Q, -0.5)
# We're assuming that select_leaf() returns a leaf like:
# root
# \
# leaf
# \
# leaf2
# which happens in this test because root is W to play and leaf was a W win.
self.assertEqual(root.position.to_play, go.WHITE)
leaf2 = root.select_leaf()
leaf2.incorporate_results(probs, -0.2, root) # another white semi-win
self.assertEqual(root.N, 3)
# average of 0, 0, -1, -0.2
self.assertAlmostEqual(root.Q, -0.3)
self.assertEqual(leaf.N, 2)
self.assertEqual(leaf2.N, 1)
# average of 0, -1, -0.2
self.assertAlmostEqual(leaf.Q, root.child_Q[leaf.fmove])
self.assertAlmostEqual(leaf.Q, -0.4)
# average of -1, -0.2
self.assertAlmostEqual(leaf.child_Q[leaf2.fmove], -0.6)
self.assertAlmostEqual(leaf2.Q, -0.6)
class MCTSPlayerMixin(object):
# If 'simulations_per_move' is nonzero, it will perform that many reads
# before playing. Otherwise, it uses 'seconds_per_move' of wall time'
def __init__(self,
board_size,
network,
seconds_per_move=5,
simulations_per_move=0,
resign_threshold=-0.90,
verbosity=0,
two_player_mode=False,
num_parallel=8):
self.board_size = board_size
self.network = network
self.seconds_per_move = seconds_per_move
self.simulations_per_move = simulations_per_move
self.verbosity = verbosity
self.two_player_mode = two_player_mode
if two_player_mode:
self.temp_threshold = -1
else:
self.temp_threshold = _get_temperature_cutoff(board_size)
self.num_parallel = num_parallel
self.qs = []
self.comments = []
self.searches_pi = []
self.root = None
self.result = 0
self.result_string = None
self.resign_threshold = -abs(resign_threshold)
def initialize_game(self, position=None):
if position is None:
position = go.Position(self.board_size)
self.root = MCTSNode(self.board_size, 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.simulations_per_move == 0:
while time.time() - start < self.seconds_per_move:
self.tree_search()
else:
current_readouts = self.root.N
while self.root.N < current_readouts + self.simulations_per_move:
self.tree_search()
if self.verbosity > 0:
print('%d: Searched %d times in %s seconds\n\n' %
(position.n, self.simulations_per_move,
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):
"""Play a move."""
# 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())
self.root = self.root.maybe_add_child(
coords.to_flat(self.board_size, c))
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[-1]
selection = random.random()
fcoord = cdf.searchsorted(selection)
assert self.root.child_N[fcoord] != 0
return coords.from_flat(self.board_size, fcoord)
def tree_search(self, num_parallel=None):
if num_parallel is None:
num_parallel = self.num_parallel
leaves = []
failsafe = 0
while len(leaves) < num_parallel and failsafe < num_parallel * 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)
def show_path_to_root(self, node):
max_depth = (self.board_size**
2) * 1.4 # 505 moves for 19x19, 113 for 9x9
pos = node.position
diff = node.position.n - self.root.position.n
if pos.recent is None:
return
def fmt(move):
return '{}-{}'.format('b' if move.color == 1 else 'w',
coords.to_kgs(self.board_size, move.move))
path = ' '.join(fmt(move) for move in pos.recent[-diff:])
if node.position.n >= max_depth:
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 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(
self.board_size,
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 is_done(self):
return self.result != 0 or self.root.is_done()
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.board_size, 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: {}".format(
default_response)
else:
return default_response