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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 unittest
import unittest.mock as mock
import numpy as np
import coords
import go
from go import Position
from coords import kgs_to_flat
from tests import test_utils
from mcts import MCTSNode
from strategies import MCTSPlayerMixin, time_recommendation
ALMOST_DONE_BOARD = test_utils.load_board('''
.XO.XO.OO
X.XXOOOO.
XXXXXOOOO
XXXXXOOOO
.XXXXOOO.
XXXXXOOOO
.XXXXOOO.
XXXXXOOOO
XXXXOOOOO
''')
SEND_TWO_RETURN_ONE = go.Position(
board=ALMOST_DONE_BOARD,
n=75,
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
)
class DummyNet():
def __init__(self, fake_priors=None, fake_value=0):
if fake_priors is None:
fake_priors = np.ones((go.N ** 2) +1) / (go.N ** 2 + 1)
self.fake_priors = fake_priors
self.fake_value = fake_value
def run(self, position):
return self.fake_priors, self.fake_value
def run_many(self, positions):
if not positions:
raise ValueError("No positions passed! (Tensorflow would have failed here.")
return [self.fake_priors] * len(positions), [self.fake_value] * len(positions)
def initialize_basic_player():
player = MCTSPlayerMixin(DummyNet())
player.initialize_game()
first_node = player.root.select_leaf()
first_node.incorporate_results(
*player.network.run(player.root.position), up_to=player.root)
return player
def initialize_almost_done_player():
probs = np.array([.001] * (go.N * go.N + 1))
probs[2:5] = 0.2 # some legal moves along the top.
probs[-1] = 0.2 # passing is also ok
net = DummyNet(fake_priors=probs)
player = MCTSPlayerMixin(net)
# root position is white to play with no history == white passed.
player.initialize_game(SEND_TWO_RETURN_ONE)
return player
class TestMCTSPlayerMixin(test_utils.MiniGoUnitTest):
def test_time_controls(self):
secs_per_move = 5
for time_limit in (10, 100, 1000):
# in the worst case imaginable, let's say a game goes 1000 moves long
move_numbers = range(0, 1000, 2)
total_time_spent = sum(
time_recommendation(move_num, secs_per_move, time_limit=time_limit)
for move_num in move_numbers)
# we should not exceed available game time
self.assertLess(total_time_spent, time_limit)
# but we should have used at least 95% of our time by the end.
self.assertGreater(total_time_spent, time_limit * 0.95)
def test_inject_noise(self):
player = initialize_basic_player()
sum_priors = np.sum(player.root.child_prior)
self.assertAlmostEqual(sum_priors, 1) # dummyNet should return normalized priors.
self.assertTrue(np.all(player.root.child_U == player.root.child_U[0]))
player.root.inject_noise()
new_sum_priors = np.sum(player.root.child_prior)
# priors should still be normalized after injecting noise
self.assertAlmostEqual(sum_priors, new_sum_priors)
# With dirichelet noise, majority of density should be in one node.
max_p = np.max(player.root.child_prior)
self.assertGreater(max_p, 3/(go.N ** 2 + 1))
def test_pick_moves(self):
player = initialize_basic_player()
root = player.root
root.child_N[coords.flatten_coords((2, 0))] = 10
root.child_N[coords.flatten_coords((1, 0))] = 5
root.child_N[coords.flatten_coords((3, 0))] = 1
root.position.n = go.N ** 2 #move 81, or 361, or... Endgame.
# Assert we're picking deterministically
self.assertTrue(root.position.n > player.temp_threshold)
move = player.pick_move()
self.assertEqual(move, (2, 0))
# But if we're in the early part of the game, pick randomly
root.position.n = 3
self.assertFalse(player.root.position.n > player.temp_threshold)
with mock.patch('random.random', lambda: .5):
move = player.pick_move()
self.assertEqual(move, (2, 0))
with mock.patch('random.random', lambda: .99):
move = player.pick_move()
self.assertEqual(move, (3,0))
def test_dont_pass_if_losing(self):
player = initialize_almost_done_player()
# check -- white is losing.
self.assertEqual(player.root.position.score(), -0.5)
for i in range(20):
player.tree_search()
# uncomment to debug this test
# print(player.root.describe())
# Search should converge on D9 as only winning move.
best_move = np.argmax(player.root.child_N)
self.assertEqual(best_move, kgs_to_flat('D9'))
# D9 should have a positive value
self.assertGreater(player.root.children[kgs_to_flat('D9')].Q, 0)
self.assertGreaterEqual(player.root.N, 20)
# passing should be ineffective.
self.assertLess(player.root.child_Q[-1], 0)
# no virtual losses should be pending
self.assertNoPendingVirtualLosses(player.root)
# uncomment to debug this test
# print(player.root.describe())
def test_parallel_tree_search(self):
player = initialize_almost_done_player()
# check -- white is losing.
self.assertEqual(player.root.position.score(), -0.5)
# initialize the tree so that the root node has populated children.
player.tree_search(num_parallel=1)
# virtual losses should enable multiple searches to happen simultaneously
# without throwing an error...
for i in range(5):
player.tree_search(num_parallel=4)
# uncomment to debug this test
# print(player.root.describe())
# Search should converge on D9 as only winning move.
best_move = np.argmax(player.root.child_N)
self.assertEqual(best_move, kgs_to_flat('D9'))
# D9 should have a positive value
self.assertGreater(player.root.children[kgs_to_flat('D9')].Q, 0)
self.assertGreaterEqual(player.root.N, 20)
# passing should be ineffective.
self.assertLess(player.root.child_Q[-1], 0)
# no virtual losses should be pending
self.assertNoPendingVirtualLosses(player.root)
def test_ridiculously_parallel_tree_search(self):
player = initialize_almost_done_player()
# Test that an almost complete game
# will tree search with # parallelism > # legal moves.
for i in range(10):
player.tree_search(num_parallel=50)
self.assertNoPendingVirtualLosses(player.root)
def test_cold_start_parallel_tree_search(self):
# Test that parallel tree search doesn't trip on an empty tree
player = MCTSPlayerMixin(DummyNet(fake_value=0.17))
player.initialize_game()
self.assertEqual(player.root.N, 0)
self.assertFalse(player.root.is_expanded)
player.tree_search(num_parallel=4)
self.assertNoPendingVirtualLosses(player.root)
# Even though the root gets selected 4 times by tree search, its
# final visit count should just be 1.
self.assertEqual(player.root.N, 1)
# 0.085 = average(0, 0.17), since 0 is the prior on the root.
self.assertAlmostEqual(player.root.Q, 0.085)
def test_tree_search_failsafe(self):
# Test that the failsafe works correctly. It can trigger if the MCTS
# repeatedly visits a finished game state.
probs = np.array([.001] * (go.N * go.N + 1))
probs[-1] = 1 # Make the dummy net always want to pass
player = MCTSPlayerMixin(DummyNet(fake_priors=probs))
pass_position = go.Position().pass_move()
player.initialize_game(pass_position)
player.tree_search(num_parallel=1)
self.assertNoPendingVirtualLosses(player.root)
def test_only_check_game_end_once(self):
# When presented with a situation where the last move was a pass,
# and we have to decide whether to pass, it should be the first thing
# we check, but not more than that.
white_passed_pos = go.Position(
).play_move((3, 3) # b plays
).play_move((3, 4) # w plays
).play_move((4, 3) # b plays
).pass_move() # w passes - if B passes too, B would lose by komi.
player = MCTSPlayerMixin(DummyNet())
player.initialize_game(white_passed_pos)
# initialize the root
player.tree_search()
# explore a child - should be a pass move.
player.tree_search()
pass_move = go.N * go.N
self.assertEqual(player.root.children[pass_move].N, 1)
self.assertEqual(player.root.child_N[pass_move], 1)
player.tree_search()
# check that we didn't visit the pass node any more times.
self.assertEqual(player.root.child_N[pass_move], 1)