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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.
import copy
import math
import numpy as np
import coords
import go
import mcts
from tests import test_utils
from absl import flags
FLAGS = flags.FLAGS
ALMOST_DONE_BOARD = test_utils.load_board('''
.XO.XO.OO
X.XXOOOO.
XXXXXOOOO
XXXXXOOOO
.XXXXOOO.
XXXXXOOOO
.XXXXOOO.
XXXXXOOOO
XXXXOOOOO
''')
TEST_POSITION = go.Position(
board=ALMOST_DONE_BOARD,
n=105,
komi=2.5,
caps=(1, 4),
ko=None,
recent=(go.PlayerMove(go.BLACK, (0, 1)),
go.PlayerMove(go.WHITE, (0, 8))),
to_play=go.BLACK
)
SEND_TWO_RETURN_ONE = go.Position(
board=ALMOST_DONE_BOARD,
n=75,
komi=0.5,
caps=(0, 0),
ko=None,
recent=(go.PlayerMove(go.BLACK, (0, 1)),
go.PlayerMove(go.WHITE, (0, 8)),
go.PlayerMove(go.BLACK, (1, 0))),
to_play=go.WHITE
)
class TestMctsNodes(test_utils.MiniGoUnitTest):
def test_upper_bound_confidence(self):
probs = np.array([.02] * (go.N * go.N + 1))
root = mcts.MCTSNode(go.Position())
leaf = root.select_leaf()
self.assertEqual(root, leaf)
leaf.incorporate_results(probs, 0.5, root)
# 0.02 are normalized to 1/82
self.assertAlmostEqual(root.child_prior[0], 1/82)
self.assertAlmostEqual(root.child_prior[1], 1/82)
puct_policy = FLAGS.c_puct * 1/82
self.assertEqual(root.N, 1)
self.assertAlmostEqual(
root.child_U[0], puct_policy * math.sqrt(1) / (1 + 0))
leaf = root.select_leaf()
self.assertNotEqual(root, leaf)
# With the first child expanded.
self.assertEqual(root.N, 1)
self.assertAlmostEqual(
root.child_U[0], puct_policy * math.sqrt(1) / (1 + 0))
self.assertAlmostEqual(
root.child_U[1], puct_policy * math.sqrt(1) / (1 + 0))
leaf.add_virtual_loss(up_to=root)
leaf2 = root.select_leaf()
self.assertNotIn(leaf2, (root, leaf))
leaf.revert_virtual_loss(up_to=root)
leaf.incorporate_results(probs, 0.3, root)
leaf2.incorporate_results(probs, 0.3, root)
# With the 2nd child expanded.
self.assertEqual(root.N, 3)
self.assertAlmostEqual(
root.child_U[0], puct_policy * math.sqrt(2) / (1 + 1))
self.assertAlmostEqual(
root.child_U[1], puct_policy * math.sqrt(2) / (1 + 1))
self.assertAlmostEqual(
root.child_U[2], puct_policy * math.sqrt(2) / (1 + 0))
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 = mcts.MCTSNode(go.Position())
white_root = mcts.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 =
8000
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_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 = mcts.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_backup_incorporate_results(self):
probs = np.array([.02] * (go.N * go.N + 1))
root = mcts.MCTSNode(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(-1 / 3, root.Q) # average of 0, 0, -1
# Leaf should have one visit
self.assertEqual(1, root.child_N[leaf.fmove])
self.assertEqual(1, leaf.N)
# 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(-0.5, root.child_Q[leaf.fmove])
self.assertAlmostEqual(-0.5, leaf.Q)
# 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(go.WHITE, root.position.to_play)
leaf2 = root.select_leaf()
leaf2.incorporate_results(probs, -0.2, root) # another white semi-win
self.assertEqual(3, root.N)
# average of 0, 0, -1, -0.2
self.assertAlmostEqual(-0.3, root.Q)
self.assertEqual(2, leaf.N)
self.assertEqual(1, leaf2.N)
# average of 0, -1, -0.2
self.assertAlmostEqual(root.child_Q[leaf.fmove], leaf.Q)
self.assertAlmostEqual(-0.4, leaf.Q)
# average of -1, -0.2
self.assertAlmostEqual(-0.6, leaf.child_Q[leaf2.fmove])
self.assertAlmostEqual(-0.6, leaf2.Q)
def test_do_not_explore_past_finish(self):
probs = np.array([0.02] * (go.N * go.N + 1), dtype=np.float32)
root = mcts.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(second_pass, node_to_explore)
def test_add_child(self):
root = mcts.MCTSNode(go.Position())
child = root.maybe_add_child(17)
self.assertIn(17, root.children)
self.assertEqual(root, child.parent)
self.assertEqual(17, child.fmove)
def test_add_child_idempotency(self):
root = mcts.MCTSNode(go.Position())
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 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 = mcts.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(1, leaf.fmove)
# 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(1, leaf.fmove)
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 = mcts.MCTSNode(go.Position())
root.incorporate_results(probs, 0, root)
root.N = 5
leaf1 = root.select_leaf()
self.assertEqual(17, leaf1.fmove)
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_normalize_policy(self):
# sum of probs > 1.0
probs = np.array([2.0] * (go.N * go.N + 1))
root = mcts.MCTSNode(TEST_POSITION)
root.incorporate_results(probs, 0, root)
root.N = 0
# Policy sums to 1.0, only legal moves have non-zero values.
self.assertAlmostEqual(1.0, sum(root.child_prior))
self.assertEqual(6, np.count_nonzero(root.child_prior))
self.assertEqual(0, sum(root.child_prior * root.illegal_moves))
def test_inject_noise_only_legal_moves(self):
probs = np.array([0.02] * (go.N * go.N + 1))
root = mcts.MCTSNode(TEST_POSITION)
root.incorporate_results(probs, 0, root)
root.N = 0
uniform_policy = 1 / sum(root.illegal_moves == 0)
expected_policy = uniform_policy * (1 - root.illegal_moves)
self.assertTrue((root.child_prior == expected_policy).all())
root.inject_noise()
# 0.75/0.25 derived from default dirichlet_noise_weight.
self.assertTrue((0.75 * expected_policy <= root.child_prior).all())
self.assertTrue(
(0.75 * expected_policy + 0.25 >= root.child_prior).all())
# Policy sums to 1.0, only legal moves have non-zero values.
self.assertAlmostEqual(1.0, sum(root.child_prior))
self.assertEqual(0, sum(root.child_prior * root.illegal_moves))