def test_legal_moves_not_ko(self):
board, player = game_init()
make_play(0, 0, board) # black
make_play(1, 0, board) # white
make_play(1, 1, board) # black
make_play(2, 0, board) # white
make_play(2, 1, board) # black
make_play(8, 8, board) # white random pos
# ○ ● ● . . .
# . ○ ○ . . .
# . . . . . .
make_play(3, 0, board) # black captures_first
# ○ . . ○ . .
# . ○ ○ . . .
# . . . . . .
mask = legal_moves(board)
self.assertEqual(board[0][0][1][0], 0) # white stone 1
self.assertEqual(board[0][0][1][1], 0) # was taken
self.assertEqual(board[0][0][2][0], 0) # white stone 2
self.assertEqual(board[0][0][2][1], 0) # was taken
self.assertEqual(board[0][0][1][2], 1) # white stone 1 was here
self.assertEqual(board[0][0][1][3], 0) # black stone was not here
self.assertEqual(board[0][0][2][2], 1) # white stone 2 was here
self.assertEqual(board[0][0][2][3], 0) # black stone was not here
self.assertEqual(mask[1], False)
self.assertEqual(mask[2], False)
def new_subtree(policy, board, parent, add_noise=False):
leaf = {}
# We need to check for legal moves here because MCTS might not have expanded
# this subtree
mask = legal_moves(board)
policy = ma.masked_array(policy, mask=mask)
# Add Dirichlet noise.
tmp = policy.reshape(-1)
if add_noise:
noise = np.random.dirichlet(
[DIRICHLET_ALPHA for i in range(tmp.shape[0])])
tmp = (1 - DIRICHLET_EPSILON) * tmp + DIRICHLET_EPSILON * noise
for move, p in enumerate(tmp):
if isinstance(p, MaskedConstant):
continue
leaf[move] = {
'count': 0,
'value': 0,
'mean_value': 0,
'p': p,
'subtree': {},
'parent': parent,
}
return leaf
def test_simulation_can_recover_from_sucide_move_white(self):
model = self.model
board, player = game_init()
x = randrange(SIZE)
y = randrange(SIZE)
for i in range(SIZE):
for j in range(SIZE):
make_play(0, SIZE, board) # Black does not play playing
if i == x and j == y:
make_play(0, SIZE, board) # pass on one intersection
else:
make_play(i, j, board)
make_play(0, SIZE, board) # Black does not play playing
policies, values = model.predict_on_batch(board)
policy = policies[0]
policy[y * SIZE + x], policy[SIZE * SIZE] = policy[
SIZE * SIZE], policy[y * SIZE + x] # Make best move sucide
mask = legal_moves(board)
policy = ma.masked_array(policy, mask=mask)
self.assertEqual(np.argmax(policy),
y * SIZE + x) # Best option in policy is sucide
tree = new_tree(policy, board)
chosen_play = select_play(policy,
board,
mcts_simulations=128,
mcts_tree=tree,
temperature=0,
model=model)
# First simulation chooses pass, second simulation chooses sucide (p is still higher),
# then going deeper it chooses pass again (value is higher)
self.assertEqual(chosen_play, SIZE * SIZE) # Pass move is best option
def select_play(policy, board, mcts_simulations, mcts_tree, temperature,
model):
mask = legal_moves(board)
policy = ma.masked_array(policy, mask=mask)
index = mcts_decision(policy, board, mcts_simulations, mcts_tree,
temperature, model)
x, y = index2coord(index)
return index
def select_play(policy, board, mcts_simulations, mcts_tree, temperature, model):
if (get_winner(board)[0]==board[0, 0, 0, -1]) and (board[:, :, :, 0]==board[:, :, :, 2]).all() and (board[:, :, :, 1]==board[:, :, :, 3]).all():
return SIZE * SIZE
mask = legal_moves(board)
policy = ma.masked_array(policy, mask=mask)
index = mcts_decision(policy, board, mcts_simulations, mcts_tree, temperature, model)
x, y = index2coord(index)
return index
def select_play(policy, board, mcts_simulations, mcts_tree, temperature,
model):
mask = legal_moves(board)
policy = ma.masked_array(policy, mask=mask)
start = datetime.datetime.now()
index = mcts_decision(policy, board, mcts_simulations, mcts_tree,
temperature, model)
end = datetime.datetime.now()
d = tree_depth(mcts_tree)
# print("################TIME PER MOVE: %s tree depth: %s" % (end - start, d))
return index
def test_legal_moves_suicide2(self):
board, player = game_init()
make_play(3, 0, board) # black = 1, col, row
make_play(1, 0, board) # white
make_play(1, 1, board) # black
make_play(2, 1, board) # white
make_play(3, 1, board, -1) # white
make_play(4, 0, board, -1) # white
# . ● . ○ ● .
# . ○ ● ● . .
# . . . . . .
mask = legal_moves(board)
self.assertEqual(mask[2], True) # suicide move should be illegal
def test_legal_moves_suicide(self):
board, player = game_init()
make_play(0, 1, board) # black
make_play(1, 0, board) # white
make_play(1, 1, board) # black
make_play(2, 1, board) # white
make_play(8, 8, board) # black random pos
make_play(3, 0, board) # white
# . ● . ● . .
# ○ ○ ● . . .
# . . . . . .
mask = legal_moves(board)
self.assertEqual(mask[2], True) # suicide move should be illegal
def test_legal_moves_not_suicide(self):
board, player = game_init()
make_play(0, 0, board) # black
make_play(1, 0, board) # white
make_play(1, 1, board) # black
make_play(2, 1, board) # white
make_play(8, 8, board) # black random pos
make_play(3, 0, board) # white
# ○ ● . ● . .
# . ○ ● . . .
# . . . . . .
mask = legal_moves(board)
self.assertEqual(mask[2],
False) # not a suicide when capture other stones
def test_legal_moves_ko(self):
board, player = game_init()
make_play(0, 0, board) # black
make_play(1, 0, board) # white
make_play(1, 1, board) # black
make_play(2, 1, board) # white
make_play(8, 8, board) # black random pos
make_play(3, 0, board) # white
# ○ ● . ● . .
# . ○ ● . . .
# . . . . . .
make_play(2, 0, board) # black captures_first
# ○ . ○ ● . .
# . ○ ● . . .
# . . . . . .
mask = legal_moves(board)
self.assertEqual(board[0][0][1][0], 0) # white stone
self.assertEqual(board[0][0][1][1], 0) # was taken
self.assertEqual(board[0][0][1][2], 1) # white stone was here
self.assertEqual(board[0][0][1][3], 0) # black stone was not here
self.assertEqual(mask[1], True)
def test_simulation_can_recover_from_sucide_move_white(self):
model = self.model
board, player = game_init()
give_two_eyes(board, 'W')
policies, values = model.predict_on_batch(board)
policy = policies[0]
if np.argmax(policy) == PASS:
policy[0], policy[PASS] = policy[PASS], policy[0] # Make best move sucide
mask = legal_moves(board)
policy = ma.masked_array(policy, mask=mask)
self.assertEqual(np.argmax(policy), 0) # Best option in policy is sucide
else:
print("Warning, policy is not great")
tree = Tree()
tree.new_tree(policy, board, move=2)
chosen_play = select_play(policy, board, mcts_simulations=128, mcts_tree=tree.tree, temperature=0, model=model)
# First simulation chooses pass, second simulation chooses sucide (p is still higher),
# then going deeper it chooses pass again (value is higher)
self.assertEqual(chosen_play, PASS) # Pass move is best option
