def debug_game():
mind = PExpMind_v3(size=9, init=False, search_params=None)
# mind.load_net('../../models/voldmaster_' + str(0))
mind.load_net('../../models/v3_0')
cache = BitBoardCache("../cache/9-magics", size=9, win_chain_length=5, force_build_win_checks=False)
board = BitBoard(cache, size=9, win_chain_length=5)
moves = [49, 40, 50, 29, 51, 52, 47, 48, 41, 31, 32, 23, 39, 68, 42, 58, 38, 33, 65, 56, 57, 73, 67, 37, 64, 66, 43,
59, 21, 22, 13, 25, 24, 44, 30, 16, 3, 12, 2, 1, 5, 6, 14, 17, 35, 7, 71, 28, 19, 34, 60, 78, 77, 62, 46,
54, 55, 36, 20, 45, 63, 18, 27, 53, 15, 9, 0, 69, 10, 75, 74, 8, 79, 72, 80]
for move in moves:
board.move(move)
print(board)
print(board)
searcher = PEvenSearch(board, mind.policy_est, mind.value_est,
search_params={
'max_iterations': 10,
'min_child_p': -6,
'p_batch_size': 1 << 10,
'q_fraction': 3
})
searcher.run()
def test_move_order_search(self):
mind = PExpMind_v3(size=9, init=False, search_params=None)
mind.load_net('../../models/voldmaster_' + str(0))
cache = BitBoardCache("../cache/9-magics",
size=9,
win_chain_length=5,
force_build_win_checks=False)
board = BitBoard(cache, size=9, win_chain_length=5)
moves = [(7, 1), (0, 1), (7, 3), (1, 7), (7, 4), (3, 1), (3, 2),
(4, 1), (3, 4), (6, 1), (6, 3), (6, 2), (5, 2), (8, 7),
(7, 7), (6, 8), (5, 8), (1, 6), (2, 7)]
for move in moves:
board.move_coord(*move)
print(board)
searcher = PEvenSearch(board,
mind.policy_est,
mind.value_est,
search_params={
'max_iterations': 10,
'min_child_p': -7,
'p_batch_size': 1 << 10,
'q_fraction': 1
})
searcher.run(3)
self.assertEqual(searcher.get_pv().calculate_pv_order(), [19, 46, 10])
def test_game_closure(self):
mind = PExpMind_v3(size=9, init=False)
mind.load_net('../../models/voldmaster_' + str(0))
cache = BitBoardCache("../cache/9-magics",
size=9,
win_chain_length=5,
force_build_win_checks=False)
board = BitBoard(cache, size=9, win_chain_length=5)
board.move_coord(1, 0)
board.move_coord(1, 1)
board.move_coord(2, 0)
board.move_coord(2, 1)
board.move_coord(3, 0)
board.move_coord(3, 1)
board.move_coord(4, 0)
board.move_coord(4, 1)
searcher = PEvenSearch(board,
mind.policy_est,
mind.value_est,
max_iterations=4,
p_batch_size=1024,
verbose=True,
validations=True)
searcher.run(4)
pv = searcher.get_pv()
self.assertEqual(len(pv.get_move_chain()), 1)
self.assertEqual(pv.get_q(), PExpNodeV3.MAX_Q)
def test_search(self):
mind = PExpMind_v3(size=9,
init=False,
verbose=True,
search_params={
'max_iterations': 10,
'min_child_p': -7,
'p_batch_size': 1 << 12,
'q_fraction': 1
})
mind.load_net('../../models/voldmaster_' + str(0))
cache = BitBoardCache("../cache/9-magics",
size=9,
win_chain_length=5,
force_build_win_checks=False)
board = BitBoard(cache, size=9, win_chain_length=5)
moves = [(6, 0), (7, 0), (3, 1), (2, 4), (0, 3), (3, 4), (2, 6),
(4, 4), (3, 5), (2, 5), (4, 5), (2, 7), (5, 5)]
for move in moves:
board.move_coord(*move)
print(board)
searcher = PEvenSearch(board, mind.policy_est, mind.value_est)
searcher.run(num_iterations=5)
pv = searcher.get_pv()
self.assertEqual(len(pv.get_move_chain()), 3)
self.assertEqual(pv.get_q(), PExpNodeV3.MIN_Q)
def play_oldmaster():
mind = PExpMind_v3(size=9,
init=False,
search_params={
'max_iterations': 3,
'min_child_p': -7,
'p_batch_size': 1 << 10,
'q_fraction': 1
})
mind.load_net('../../models/voldmaster_' + str(0))
return mind
def make_v3(min_child_p=-7, size=9):
mind = PExpMind_v3(size=size,
init=False,
search_params={
'max_iterations': 10,
'min_child_p': min_child_p,
'p_batch_size': 1 << 10,
'num_pv_expand': 25,
'q_fraction': 1
})
mind.load_net('../../models/v3_0_1')
return mind
def test_even_search(self):
mind = PExpMind_v3(size=9, init=False, search_params=None)
mind.load_net('../../models/voldmaster_' + str(0))
board = Board(size=9, win_chain_length=5)
searcher = PEvenSearch(board,
mind.policy_est,
mind.value_est,
max_iterations=2,
p_batch_size=1024,
verbose=True,
validations=True)
searcher.p_expand()
self.assertGreater(len(searcher.expandable_nodes), 0)
for node in searcher.expandable_nodes:
self.assertEqual(list(node.get_parents())[0], searcher.root_node)
self.assertLess(node.log_total_p, 0)
searcher.q_eval()
self.assertNotEqual(searcher.root_node.get_principal_variation(), None)
def test_able_to_draw(self):
mind = PExpMind_v3(size=9, init=False, search_params=None)
mind.load_net('../../models/voldmaster_' + str(0))
cache = BitBoardCache("../cache/9-magics",
size=9,
win_chain_length=5,
force_build_win_checks=False)
board = BitBoard(cache, size=9, win_chain_length=5)
# uninteresting moves for P
moves = [
37, 45, 74, 50, 47, 29, 31, 39, 49, 58, 48, 41, 40, 32, 22, 13, 23,
59, 68, 21, 60, 36, 5, 42, 34, 20, 67, 57, 56, 65, 52, 44, 38, 12,
30, 14, 11, 15, 16, 43, 46, 54, 66, 18, 27, 72, 63, 28, 4, 69, 76,
55, 75, 77, 25, 7, 24, 26, 53, 61, 78, 73, 19, 8, 64, 35, 62, 79,
51, 6, 17, 3
]
self.just_no_crash_from_position(mind, board, moves, num_iterations=2)
# last move on the board
moves = [
70, 69, 10, 42, 43, 25, 50, 40, 49, 48, 32, 41, 39, 58, 38, 31, 51,
22, 13, 52, 21, 29, 67, 59, 56, 37, 61, 30, 28, 47, 12, 14, 11, 9,
33, 3, 23, 53, 27, 24, 6, 46, 57, 19, 55, 2, 44, 34, 4, 74, 7, 36,
5, 8, 54, 62, 16, 65, 66, 78, 75, 68, 1, 15, 76, 77, 26, 35, 45,
63, 18, 60, 79, 20, 0, 64, 80, 72, 73, 17
]
self.just_no_crash_from_position(mind, board, moves, num_iterations=2)
# instant win
moves = [23, 66, 44, 80, 35, 12, 33, 30, 31, 39, 40, 48, 75]
self.just_no_crash_from_position(mind, board, moves, num_iterations=2)
# no leaf nodes
moves = [41, 8, 2, 29, 73, 31, 49, 40, 33, 50, 30, 39, 57, 22]
self.just_no_crash_from_position(mind, board, moves, num_iterations=2)
def test_pnet(self):
mind = PExpMind_v3(size=9, init=False, search_params=None)
mind.load_net('../../models/v3_' + str(0))
cache = BitBoardCache("../cache/9-magics",
size=9,
win_chain_length=5,
force_build_win_checks=False)
board = BitBoard(cache, size=9, win_chain_length=5)
fboard = FeatureBoard_v1_1(board)
for move in [76, 65, 58, 27, 40]:
board.move(move)
fboard.move(move)
print(board)
mind.make_move(board)
predictions = mind.policy_est.predict(
[np.array([fboard.get_q_features()])])[0]
self.assertEqual(np.argmax(predictions), 49)
print(sorted(enumerate(predictions), key=lambda x: x[1], reverse=True))
def play():
SIZE = 9
mind = PExpMind_v3(size=9,
init=False,
search_params={
'max_iterations': 10,
'min_child_p': -7,
'p_batch_size': 1 << 10,
'num_pv_expand': 25,
'q_fraction': 1
})
mind.load_net('../../models/v3_' + str(0))
cache = BitBoardCache("../cache/9-magics",
size=9,
win_chain_length=5,
force_build_win_checks=False)
board = BitBoard(cache, size=9, win_chain_length=5)
# randomize the board a bit
for j in range(random.randint(0, int(SIZE * 2))):
board.make_random_move()
# board.move_coord(4, 4)
print(board.pprint())
while True:
if board.get_player_to_move() == Player.FIRST:
inp = input("Input your move (i.e. \"3 5\"): ")
if len(inp.split(' ')) != 2:
print('Incorrect number of coordinates, please try again!')
continue
x, y = inp.split(' ')
try:
x = int(x)
y = int(y)
except:
print('Please input Numbers!')
continue
if x < 0 or x >= SIZE or y < 0 or y >= SIZE:
print('Out of bounds!')
continue
index = board._transformer.coordinate_to_index(x, y)
if not board.is_move_available(index):
print('Invalid Move!')
continue
result = board.move_coord(x, y)
print(board)
else:
print('Computer is thinking...')
print(board._ops)
move, current_q, best_q = mind.make_move(board)
print(" ")
print(move, 'Q:', best_q)
# if best_q > PExpNode.MAX_MODEL_Q :
# print('Computer Resigns!')
# break
board.move(move)
print(board)
if board.game_over():
if board.get_winning_player() == Player.FIRST:
print('COMPUTER WINS!')
elif board.get_winning_player() == Player.SECOND:
print('YOU WIN!')
print("DRAW!")
break