def test_move_is_hashable(self):
moves = {
Move.play(Point(1, 1)): 1,
Move.resign(): 2,
}
self.assertEqual(1, moves[Move.play(Point(1, 1))])
self.assertEqual(2, moves[Move.resign()])
def read_move(self):
is_play = self.read_bool()
is_pass = self.read_bool()
is_resign = self.read_bool()
if is_play:
row = self.read_int()
col = self.read_int()
return Move.play(Point(row=row, col=col))
if is_pass:
return Move.pass_turn()
assert is_resign
return Move.resign()
def select_move(self, game_state):
start = time.time()
self.root = None
if self._ladder_rollouts > 0:
self.root = self.read_ladders(game_state, self._ladder_rollouts)
if self.root is None:
self.root = self.create_node(game_state, add_noise=True)
num_rollouts = 0
while num_rollouts < self._num_rollouts:
to_expand = set()
batch_count = 0
while batch_count < self._batch_size:
# Find a leaf.
node = self.root
move = self.select_branch(node)
while node.has_child(move):
node.add_virtual_loss(move)
node = node.get_child(move)
move = self.select_branch(node)
node.add_virtual_loss(move)
batch_count += 1
to_expand.add((node, move))
batch_num_visits = len(to_expand)
new_children = self.create_children(to_expand)
for new_child in new_children:
new_child.parent.record_visit(new_child.move, new_child.value)
num_rollouts += batch_num_visits
# Now select a move in proportion to how often we visited it.
visit_counts = self.root.visit_counts
expected_values = calc_expected_values(self.root.total_values,
visit_counts)
tiebreak = 0.499 * (expected_values + 1)
decide_vals = visit_counts + tiebreak
for move_idx in np.argsort(decide_vals):
visit_count = visit_counts[move_idx]
if visit_count > 0:
sys.stderr.write('{}: {:.3f} {}\n'.format(
format_move(self._encoder.decode_move_index(move_idx)),
expected_values[move_idx], visit_count))
temperature = self._temp_schedule.get(game_state.num_moves)
if temperature > 0:
move_indices, = np.where(visit_counts > 0)
raw_counts = decide_vals[move_indices]
p = np.power(raw_counts, 1.0 / temperature)
p /= np.sum(p)
move_index = np.random.choice(move_indices, p=p)
else:
move_index = np.argmax(decide_vals)
self._log_pv(self.root)
chosen_move = self._encoder.decode_move_index(move_index)
sys.stderr.write('Select {} Q {:.3f}\n'.format(
format_move(chosen_move), expected_values[move_index]))
end = time.time()
sys.stderr.write('Decided in {:.3f}s\n'.format(end - start))
sys.stderr.flush()
if expected_values[move_index] < self._resign_below:
sys.stderr.write('Resigning because Q {:.3f} < {:.3f}\n'.format(
expected_values[move_index], self._resign_below))
return Move.resign()
if self._gracious_winner is not None:
if game_state.last_move is not None and game_state.last_move == Move.pass_turn(
):
pass_idx = self._encoder.encode_move(Move.pass_turn())
if visit_counts[pass_idx] >= 2 and \
expected_values[pass_idx] > self._gracious_winner:
sys.stderr.write('Pass has Q {:.3f}\n'.format(
expected_values[pass_idx]))
return Move.pass_turn()
return chosen_move