def get_after_states(self, current_state):
after_states = []
# Horizontal placements
max_col_index = self.num_columns - 2
for col_ix, free_pos in enumerate(
current_state.lowest_free_rows[:max_col_index]):
anchor_row = np.maximum(
current_state.lowest_free_rows[col_ix] - 1,
np.max(current_state.lowest_free_rows[(col_ix + 1):(col_ix +
3)]))
new_representation = current_state.representation.copy()
new_representation[anchor_row, (col_ix + 1):(col_ix + 3)] = 1
new_representation[anchor_row + 1, col_ix:(col_ix + 2)] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix:(col_ix + 2)] = anchor_row + 2
new_lowest_free_rows[col_ix + 2] = anchor_row + 1
new_state = state.State(representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(
anchor_row, anchor_row + 1),
pieces_per_changed_row=np.array([2]),
landing_height_bonus=0.5,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
# Vertical placements
max_col_index = self.num_columns - 1
for col_ix, free_pos in enumerate(
current_state.lowest_free_rows[:max_col_index]):
anchor_row = np.maximum(
current_state.lowest_free_rows[col_ix],
current_state.lowest_free_rows[col_ix + 1] - 1)
new_representation = current_state.representation.copy()
new_representation[anchor_row:(anchor_row + 2), col_ix] = 1
new_representation[(anchor_row + 1):(anchor_row + 3),
col_ix + 1] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix] = anchor_row + 2
new_lowest_free_rows[col_ix + 1] = anchor_row + 3
new_state = state.State(representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(
anchor_row, anchor_row + 2),
pieces_per_changed_row=np.array([1, 2]),
landing_height_bonus=1,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
return after_states
def get_after_states(self, current_state):
after_states = []
# Vertical placements
for col_ix, free_pos in enumerate(current_state.lowest_free_rows):
anchor_row = free_pos
new_representation = current_state.representation.copy()
new_representation[anchor_row:(anchor_row + 4), col_ix] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix] += 4
new_state = state.State(
representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(anchor_row, anchor_row + 4),
pieces_per_changed_row=np.array([1, 1, 1, 1]),
landing_height_bonus=1.5,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
# Horizontal placements
max_col_index = self.num_columns - 3
for col_ix, free_pos in enumerate(
current_state.lowest_free_rows[:max_col_index]):
anchor_row = np.max(current_state.lowest_free_rows[col_ix:(col_ix +
4)])
new_representation = current_state.representation.copy()
new_representation[anchor_row, col_ix:(col_ix + 4)] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix:(col_ix + 4)] = anchor_row + 1
new_state = state.State(representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(
anchor_row, anchor_row + 1),
pieces_per_changed_row=np.array([4]),
landing_height_bonus=0,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
return after_states
def reset(self):
self.game_over = False
self.current_state = state.State(representation=np.zeros(
(self.num_rows + self.tetromino_size, self.num_columns),
dtype=np.int_),
lowest_free_rows=np.zeros(
self.num_columns, dtype=np.int_),
num_features=self.num_features,
feature_type=self.feature_type)
self.tetromino_sampler = tetromino.TetrominoSampler(self.tetrominos)
self.cleared_lines = 0
self.state_samples = []
def __init__(self,
num_columns,
num_rows,
player,
verbose,
plot_intermediate_results=False,
tetromino_size=4,
target_update=1,
max_cleared_test_lines=np.inf):
self.num_columns = num_columns
self.num_rows = num_rows
self.tetromino_size = tetromino_size
self.player = player
self.verbose = verbose
self.target_update = target_update
self.num_features = self.player.num_features
self.feature_type = self.player.feature_type
self.n_fields = self.num_columns * self.num_rows
self.game_over = False
self.current_state = state.State(representation=np.zeros(
(self.num_rows + self.tetromino_size, self.num_columns),
dtype=np.int_),
lowest_free_rows=np.zeros(
self.num_columns, dtype=np.int_),
num_features=self.num_features,
feature_type=self.feature_type)
self.tetrominos = [
tetromino.Straight(self.feature_type, self.num_features,
self.num_columns),
tetromino.RCorner(self.feature_type, self.num_features,
self.num_columns),
tetromino.LCorner(self.feature_type, self.num_features,
self.num_columns),
tetromino.Square(self.feature_type, self.num_features,
self.num_columns),
tetromino.SnakeR(self.feature_type, self.num_features,
self.num_columns),
tetromino.SnakeL(self.feature_type, self.num_features,
self.num_columns),
tetromino.T(self.feature_type, self.num_features, self.num_columns)
]
self.tetromino_sampler = tetromino.TetrominoSamplerRandom(
self.tetrominos)
self.cleared_lines = 0
self.state_samples = []
self.cumulative_steps = 0
self.max_cleared_test_lines = max_cleared_test_lines
self.plot_intermediate_results = plot_intermediate_results
def reset(self):
self.game_over = False
self.current_state = state.State(
np.zeros((self.num_rows, self.num_columns),
dtype=np.bool_), # representation=
np.zeros(self.num_columns, dtype=np.int64), # lowest_free_rows=
np.array([0], dtype=np.int64), # changed_lines=
np.array([0], dtype=np.int64), # pieces_per_changed_row=
0.0, # landing_height_bonus=
self.num_features, # num_features=
"bcts", # feature_type=
False, # terminal_state=
False # has_overlapping_fields=
)
self.current_state.calc_bcts_features()
self.cleared_lines = 0
self.generative_model.next_tetromino()
def load_rollout_state_population(p, max_samples, print_average_height=False):
sample_list_save_name = p.rollout_population_path
with open(sample_list_save_name, "r") as ins:
rollout_population = []
count = 0
for x in ins:
if count < max_samples:
# print(count)
rep = np.vstack((np.array([
np.array([int(z) for z in bin(int(y))[3:13]])
for y in x.split()
]), np.zeros((4, p.num_columns))))
rep = rep.astype(np.bool_)
lowest_free_rows = calc_lowest_free_rows(rep)
rollout_population.append(
state.State(
rep,
lowest_free_rows,
np.array([0], dtype=np.int64), # changed_lines=
np.array([0],
dtype=np.int64), # pieces_per_changed_row=
0.0, # landing_height_bonus=
8, # num_features=
"bcts", # feature_type=
False # terminal_state=
))
count += 1
else:
break
# print(f"Succesfully loaded {count} rollout starting states!")
if print_average_height:
average_lowest_free_rows = np.mean(
[np.mean(d.lowest_free_rows) for d in rollout_population])
print("average height in rollout state population",
average_lowest_free_rows)
return rollout_population
def __init__(self,
num_columns,
num_rows,
max_cleared_test_lines=10e9,
tetromino_size=4,
feature_type="bcts",
num_features=8):
"""
:param num_columns:
:param num_rows:
:param tetromino_size:
:param max_cleared_test_lines:
"""
self.num_columns = num_columns
self.num_rows = num_rows
self.tetromino_size = tetromino_size
self.num_features = num_features
self.feature_type = feature_type
self.max_cleared_test_lines = max_cleared_test_lines
self.game_over = False
self.current_state = state.State(
np.zeros((self.num_rows, self.num_columns),
dtype=np.bool_), # representation=
np.zeros(self.num_columns, dtype=np.int64), # lowest_free_rows=
np.array([0], dtype=np.int64), # changed_lines=
np.array([0], dtype=np.int64), # pieces_per_changed_row=
0.0, # landing_height_bonus=
self.num_features, # num_features=
"bcts", # feature_type=
False, # terminal_state=
False # has_overlapping_fields=
)
self.generative_model = tetromino.Tetromino(self.feature_type,
self.num_features,
self.num_columns)
self.cleared_lines = 0
def get_after_states(self, current_state):
after_states = []
max_col_index = self.num_columns - 2
for col_ix, free_pos in enumerate(
current_state.lowest_free_rows[:max_col_index]):
# Bottom-left corner (= 'hole' in top-right corner)
anchor_row = np.max(current_state.lowest_free_rows[col_ix:(col_ix +
3)])
new_representation = current_state.representation.copy()
new_representation[anchor_row, col_ix:(col_ix + 3)] = 1
new_representation[anchor_row + 1, col_ix] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix] = anchor_row + 2
new_lowest_free_rows[(col_ix + 1):(col_ix + 3)] = anchor_row + 1
new_state = state.State(representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(
anchor_row, anchor_row + 1),
pieces_per_changed_row=np.array([3]),
landing_height_bonus=0.5,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
# Top-right corner
anchor_row = np.maximum(
np.max(current_state.lowest_free_rows[col_ix:(col_ix + 2)]) -
1, current_state.lowest_free_rows[col_ix + 2])
new_representation = current_state.representation.copy()
new_representation[anchor_row + 1, col_ix:(col_ix + 3)] = 1
new_representation[anchor_row, col_ix + 2] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix:(col_ix + 3)] = anchor_row + 2
new_state = state.State(representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(
anchor_row, anchor_row + 2),
pieces_per_changed_row=np.array([1, 3]),
landing_height_bonus=0.5,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
# Vertical placements. 'height' becomes 'width' :)
max_col_index = self.num_columns - 1
for col_ix, free_pos in enumerate(
current_state.lowest_free_rows[:max_col_index]):
# Top-left corner
anchor_row = np.maximum(
current_state.lowest_free_rows[col_ix],
current_state.lowest_free_rows[col_ix + 1] - 2)
new_representation = current_state.representation.copy()
new_representation[anchor_row + 2, col_ix + 1] = 1
new_representation[anchor_row:(anchor_row + 3), col_ix] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix:(col_ix + 2)] = anchor_row + 3
new_state = state.State(representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(
anchor_row, anchor_row + 3),
pieces_per_changed_row=np.array([1, 1, 2]),
landing_height_bonus=1,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
# Bottom-right corner
anchor_row = np.max(current_state.lowest_free_rows[col_ix:(col_ix +
2)])
new_representation = current_state.representation.copy()
new_representation[anchor_row:(anchor_row + 3), col_ix + 1] = 1
new_representation[anchor_row, col_ix] = 1
new_lowest_free_rows = current_state.lowest_free_rows.copy()
new_lowest_free_rows[col_ix + 1] = anchor_row + 3
new_lowest_free_rows[col_ix] = anchor_row + 1
new_state = state.State(representation=new_representation,
lowest_free_rows=new_lowest_free_rows,
anchor_col=col_ix,
changed_lines=np.arange(
anchor_row, anchor_row + 1),
pieces_per_changed_row=np.array([2]),
landing_height_bonus=1,
num_features=self.num_features,
feature_type=self.feature_type)
after_states.append(new_state)
return after_states
[0, 0, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 0, 0, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0, 1, 1, 1],
[0, 1, 0, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0, 1, 1, 1, 1],
[1, 0, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 0, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 1, 0, 1, 1]])
lowest_free_rows = calc_lowest_free_rows(representation)
st = state.State(representation=representation, lowest_free_rows=lowest_free_rows,
changed_lines=np.array([0], dtype=np.int64),
pieces_per_changed_row=np.array([0], dtype=np.int64),
landing_height_bonus=0.0,
num_features=8,
feature_type="bcts",
terminal_state=False, # this is useful to generate a "terminal state"
has_overlapping_fields=False)
print(print_board_to_string(st))
tet = tetromino.Tetromino(feature_type="bcts", num_features=8, num_columns=10)
print(print_tetromino(tet.current_tetromino))
af_st = tet.get_after_states(st)
print(print_board_to_string(af_st[0]))
