def test_select_leaf(self):
probs = np.array([.02] * (go.N * go.N + 1))
probs[kgs_to_flat('D9')] = 0.4
root = MCTSNode(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[kgs_to_flat('D9')])
def test_select_leaf(self):
flattened = coords.to_flat(coords.from_kgs('D9'))
probs = np.array([.02] * (go.N * go.N + 1))
probs[flattened] = 0.4
root = MCTSNode(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_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] * (go.N * go.N + 1), dtype=np.float32)
root = MCTSNode(go.Position())
root.select_leaf().incorporate_results(probs, 0, root)
first_pass = root.maybe_add_child(coords.flatten_coords(None))
first_pass.incorporate_results(probs, 0, root)
second_pass = first_pass.maybe_add_child(coords.flatten_coords(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_do_not_explore_past_finish(self):
probs = np.array([0.02] * (go.N * go.N + 1), dtype=np.float32)
root = MCTSNode(go.Position())
root.select_leaf().incorporate_results(probs, 0, root)
first_pass = root.maybe_add_child(coords.to_flat(None))
first_pass.incorporate_results(probs, 0, root)
second_pass = first_pass.maybe_add_child(coords.to_flat(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_dont_pick_unexpanded_child(self):
probs = np.array([0.001] * (go.N * go.N + 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(go.Position())
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_dont_pick_unexpanded_child(self):
probs = np.array([0.001] * (go.N * go.N + 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(go.Position())
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)
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)
def test_never_select_illegal_moves(self):
probs = np.array([0.02] * (go.N * go.N + 1))
# let's say the NN were to accidentally put a high weight on an illegal move
probs[1] = 0.99
root = MCTSNode(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 i in range(10):
root.inject_noise()
leaf = root.select_leaf()
self.assertNotEqual(leaf.fmove, 1)
def test_never_select_illegal_moves(self):
probs = np.array([0.02] * (go.N * go.N + 1))
# let's say the NN were to accidentally put a high weight on an illegal move
probs[1] = 0.99
root = MCTSNode(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 i in range(10):
root.inject_noise()
leaf = root.select_leaf()
self.assertNotEqual(leaf.fmove, 1)
def test_action_flipping(self):
np.random.seed(1)
probs = np.array([.02] * (go.N * go.N + 1))
probs = probs + np.random.random([go.N * go.N + 1]) * 0.001
black_root = MCTSNode(go.Position())
white_root = MCTSNode(go.Position(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_action_flipping(self):
np.random.seed(1)
probs = np.array([.02] * (go.N * go.N + 1))
probs = probs + np.random.random([go.N * go.N + 1]) * 0.001
black_root = MCTSNode(go.Position())
white_root = MCTSNode(go.Position(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)
class MCTSPlayerMixin:
# 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, network, seconds_per_move=5, simulations_per_move=0,
resign_threshold=-0.90, verbosity=0, two_player_mode=False,
num_parallel=8):
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 = TEMPERATURE_CUTOFF
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)
super().__init__()
def initialize_game(self, position=None):
if position is None:
position = go.Position()
self.root = 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.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):
'''
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(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(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):
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 >= 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(pos.recent, self.result_string,
white_name=self.network.name or "Unknown",
black_name=self.network.name 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 MCTSPlayerMixin:
# 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, network, seconds_per_move=5, simulations_per_move=0,
resign_threshold=-0.90, verbosity=0, two_player_mode=False,
num_parallel=8):
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 = TEMPERATURE_CUTOFF
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)
super().__init__()
def initialize_game(self, position=None):
if position is None:
position = go.Position()
self.root = 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.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):
'''
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[-1]
selection = random.random()
fcoord = cdf.searchsorted(selection)
assert self.root.child_N[fcoord] != 0
return coords.from_flat(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):
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 >= 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(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.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
