def test_long_simulation():
start_state = TakeOneTwiceGame(1500)
playout = Playout()
value = playout.simulate(start_state)
assert value == 1
def test_playout():
""" Just checking that playouts don't raise an exception. """
game = OthelloGame()
board = game.create_board()
playout = Playout(game)
playout.simulate(board)
def test_simulate_finished_game_for_o_player():
start_board = TicTacToeState("""\
XX.
OOO
.X.
""")
expected_value = -1
playout = Playout()
value = playout.simulate(start_board)
assert value == expected_value
def test_two_moves_per_turn():
start_board = TakeOneTwiceGame(2)
playout = Playout()
iteration_count = 10
expected_value_total = 10
value_total = 0
for _ in range(iteration_count):
value = playout.simulate(start_board)
value_total += value
assert value_total == expected_value_total
def test_analyse_finished_game():
board = TicTacToeState("""\
OXO
XXO
XOX
""")
heuristic = Playout()
expected_value = 0 # A tie
expected_policy = [1 / 9] * 9
value, policy = heuristic.analyse(board)
assert expected_value == value
assert expected_policy == policy.tolist()
def test_analyse_finished_game():
game = TicTacToeGame()
board = game.create_board("""\
OXO
XXO
XOX
""")
heuristic = Playout(game)
expected_value = 0 # A tie
expected_policy = [1/9] * 9
value, policy = heuristic.analyse(board)
assert expected_value == value
assert expected_policy == policy.tolist()
def test_simulate_wins():
np.random.seed(0)
start_board = TicTacToeState("""\
XOX
XO.
O..
""")
iteration_count = 100
expected_value_total = -iteration_count / 3
expected_low = expected_value_total * 1.1
expected_high = expected_value_total * 0.9
playout = Playout()
value_total = 0
for _ in range(iteration_count):
value = playout.simulate(start_board)
value_total += value
assert expected_low < value_total < expected_high
def test_search_manager_reuses_node():
start_state = TicTacToeState()
manager = SearchManager(start_state, Playout())
manager.search(start_state, iterations=10)
move = manager.get_best_move()
state2 = start_state.make_move(move)
node = manager.current_node
first_value_count = node.value_count
manager.search(state2, iterations=10)
second_value_count = node.value_count
assert first_value_count > 0
assert first_value_count + 10 == second_value_count
def test_search_manager_with_opponent():
""" Like when opponent is not sharing the SearchManager. """
start_state = TicTacToeState()
manager = SearchManager(start_state, Playout())
manager.search(start_state, iterations=10)
node = manager.current_node.children[0] # Didn't call get_best_move().
move = 0
state2 = start_state.make_move(move)
first_value_count = node.value_count
manager.search(state2, iterations=10)
second_value_count = node.value_count
assert first_value_count > 0
assert first_value_count + 10 == second_value_count
def load_heuristics():
heuristics = [('Computer', Playout())]
# entry: EntryPoint
# for entry in iter_entry_points('zero_play.heuristic'):
# try:
# heuristic_class = entry.load()
# except ImportError as ex:
# library_path = os.environ.get('LD_LIBRARY_PATH')
# if library_path is not None:
# raise
# message = (f'Unable to load entry {entry.name}. Do you need to'
# f' set LD_LIBRARY_PATH?')
# raise ImportError(message) from ex
# try:
# heuristic: Heuristic = heuristic_class(start_state)
# except ValueError:
# continue
# heuristics.append((entry.name, heuristic))
return heuristics
def test_playout():
""" Just checking that playouts don't raise an exception. """
board = OthelloState()
playout = Playout()
playout.simulate(board)