def draw_info(batch, window_width, window_height, upper_grid_coord, state):
turn = GoGame.get_turn(state)
turn_str = 'B' if turn == BLACK else 'W'
prev_player_passed = GoGame.get_prev_player_passed(state)
game_ended = GoGame.get_game_ended(state)
info_label = "Turn: {}\nPassed: {}\nGame: {}".format(
turn_str, prev_player_passed, "OVER" if game_ended else "ONGOING")
pyglet.text.Label(info_label,
font_name='Helvetica',
font_size=11,
x=window_width - 20,
y=window_height - 20,
anchor_x='right',
anchor_y='top',
color=(0, 0, 0, 192),
batch=batch,
width=window_width / 2,
align='right',
multiline=True)
# Areas
black_area, white_area = GoGame.get_areas(state)
pyglet.text.Label("{}B | {}W".format(black_area, white_area),
font_name='Helvetica',
font_size=16,
x=window_width / 2,
y=upper_grid_coord + 80,
anchor_x='center',
color=(0, 0, 0, 192),
batch=batch,
width=window_width,
align='center')
def step(self, action):
'''
Assumes the correct player is making a move. Black goes first.
return observation, reward, done, info
'''
if action is None:
action = self.size**2
elif isinstance(action, tuple) or isinstance(
action, list) or isinstance(action, np.ndarray):
assert action[0] >= 0 and action[1] >= 0
assert action[0] < self.size and action[1] < self.size
action = action[0] * self.size + action[1]
if self.children is not None:
valid_moves = self.get_valid_moves()
child_idx = int(np.sum(valid_moves[:action]))
self.state, self.group_map = self.children[
child_idx], self.child_groupmaps[child_idx]
else:
self.state, self.group_map = GoGame.get_next_state(self.state,
action,
self.group_map,
inplace=True)
self.clear_cache()
return np.copy(self.state), self.get_reward(), GoGame.get_game_ended(
self.state), self.get_info()
def get_info(self):
"""
:return: Debugging info for the state
"""
return {
'prev_player_passed': GoGame.get_prev_player_passed(self.state),
'turn': 'b' if GoGame.get_turn(self.state) == GoEnv.govars.BLACK else 'w',
'game_ended': GoGame.get_game_ended(self.state)
}
def cache_children(self, canonical=False):
"""
:return: Same as get_children, but in canonical form
"""
self.children, self.child_groupmaps = GoGame.get_children(
self.state, self.group_map)
children = self.children.copy()
child_groupmaps = self.child_groupmaps.copy()
if canonical:
for i in range(len(children)):
children[i] = GoGame.get_canonical_form(children[i])
return children, child_groupmaps
def __init__(self, size, reward_method='real', black_first=True):
'''
@param reward_method: either 'heuristic' or 'real'
heuristic: gives # black pieces - # white pieces.
real: gives 0 for in-game move, 1 for winning, -1 for losing,
0 for draw, all from black player's perspective
'''
self.size = size
self.state = GoGame.get_init_board(size, black_first)
self.reward_method = RewardMethod(reward_method)
self.observation_space = gym.spaces.Box(0, 6, shape=(6, size, size))
self.action_space = gym.spaces.Discrete(
GoGame.get_action_size(self.state))
self.group_map = np.empty(self.state.shape[1:], dtype=object)
self.clear_cache()
def step(self, action):
'''
Assumes the correct player is making a move. Black goes first.
return observation, reward, done, info
'''
if action is None:
action = self.size**2
elif isinstance(action, tuple) or isinstance(
action, list) or isinstance(action, np.ndarray):
assert action[0] >= 0 and action[1] >= 0
assert action[0] < self.size and action[1] < self.size
action = action[0] * self.size + action[1]
self.state = GoGame.get_next_state(self.state, action)
return np.copy(self.state), self.get_reward(), GoGame.get_game_ended(
self.state), self.get_info()
def __init__(self, size, komi=0, reward_method='real'):
'''
@param reward_method: either 'heuristic' or 'real'
heuristic: gives # black pieces - # white pieces.
real: gives 0 for in-game move, 1 for winning, -1 for losing,
0 for draw, all from black player's perspective
'''
self.size = size
self.komi = komi
self.state = GoGame.get_init_board(size)
self.reward_method = RewardMethod(reward_method)
self.observation_space = gym.spaces.Box(np.float32(0), np.float32(govars.NUM_CHNLS),
shape=(govars.NUM_CHNLS, size, size))
self.action_space = gym.spaces.Discrete(GoGame.get_action_size(self.state))
self.group_map = [set(), set()]
self.done = False
def reset(self, black_first=True):
'''
Reset state, go_board, curr_player, prev_player_passed,
done, return state
'''
self.state = GoGame.get_init_board(self.size, black_first)
self.group_map = np.empty(self.state.shape[1:], dtype=object)
self.clear_cache()
return np.copy(self.state)
def reset(self):
'''
Reset state, go_board, curr_player, prev_player_passed,
done, return state
'''
self.state = GoGame.get_init_board(self.size)
self.group_map = [set(), set()]
self.done = False
return np.copy(self.state)
def step(self, action):
'''
Assumes the correct player is making a move. Black goes first.
return observation, reward, done, info
'''
assert not self.done
if isinstance(action, tuple) or isinstance(action, list) or isinstance(action, np.ndarray):
assert 0 <= action[0] < self.size
assert 0 <= action[1] < self.size
action = self.size * action[0] + action[1]
elif action is None:
action = self.size ** 2
actions = np.array([action])
states, group_maps = GoGame.get_next_states(self.state, actions, self.group_map)
self.state, self.group_map = states[0], group_maps[0]
self.done = GoGame.get_game_ended(self.state)
return np.copy(self.state), self.get_reward(), self.done, self.get_info()
def reset(self, black_first=True, state=None):
'''
Reset state, go_board, curr_player, prev_player_passed,
done, return state
'''
if state is None:
self.state = GoGame.get_init_board(self.size, black_first)
else:
assert state.shape[1] == self.size
self.state = np.copy(state)
return np.copy(self.state)
def __init__(self,
size,
reward_method='real',
black_first=True,
state=None):
'''
@param reward_method: either 'heuristic' or 'real'
heuristic: gives # black pieces - # white pieces.
real: gives 0 for in-game move, 1 for winning, -1 for losing,
0 for draw, all from black player's perspective
'''
self.size = size
if state is None:
self.state = GoGame.get_init_board(size, black_first)
else:
assert state.shape[1] == size
self.state = np.copy(state)
self.reward_method = RewardMethod(reward_method)
self.observation_space = gym.spaces.Box(0, 6, shape=(6, size, size))
self.action_space = gym.spaces.Discrete(
GoGame.get_action_size(self.state))
def step_batch(self, state, action):
'''
Assumes the correct player is making a move. Black goes first.
return observation, reward, done, info
But next step will not change the previous state
'''
assert not self.done
if isinstance(action, tuple) or isinstance(action, list) or isinstance(
action, np.ndarray):
assert 0 <= action[0] < self.size
assert 0 <= action[1] < self.size
action = self.size * action[0] + action[1]
elif action is None:
action = self.size**2
actions = np.array([action])
next_states, next_group_maps = GoGame.get_batch_next_states(
state, actions, self.group_map)
next_state, next_group_map = next_states[0], next_group_maps[0]
next_done = GoGame.get_game_ended(next_state)
return np.copy(next_state), self.get_reward_batch(
next_state, next_done), next_done, self.get_info_batch(next_state)
def get_winning(self):
"""
:return: Who's currently winning, regardless if the game is over
"""
black_area, white_area = GoGame.get_areas(self.state)
area_difference = black_area - white_area
if area_difference > 0:
return 1
elif area_difference == 0:
return 0.5
else:
assert area_difference < 0
return 0
def get_reward(self):
'''
Return reward based on reward_method.
heuristic: black total area - white total area
real: 0 for in-game move, 1 for winning, 0 for losing,
0.5 for draw, from black player's perspective.
Winning and losing based on the Area rule
Also known as Trump Taylor Scoring
Area rule definition: https://en.wikipedia.org/wiki/Rules_of_Go#End
'''
if self.reward_method == RewardMethod.REAL:
return self.get_winner()
elif self.reward_method == RewardMethod.HEURISTIC:
black_area, white_area = GoGame.get_areas(self.state)
area_difference = black_area - white_area
if self.game_ended():
return (1 if area_difference > 0 else -1) * self.size**2
return area_difference
else:
raise Exception("Unknown Reward Method")
def turn(self):
return GoGame.get_turn(self.state)
class GoEnv(gym.Env):
metadata = {'render.modes': ['terminal', 'human']}
gogame = GoGame()
govars = govars
def __init__(self, size, reward_method='real'):
'''
@param reward_method: either 'heuristic' or 'real'
heuristic: gives # black pieces - # white pieces.
real: gives 0 for in-game move, 1 for winning, -1 for losing,
0 for draw, all from black player's perspective
'''
self.size = size
self.state = GoGame.get_init_board(size)
self.reward_method = RewardMethod(reward_method)
self.observation_space = gym.spaces.Box(0,
govars.NUM_CHNLS,
shape=(govars.NUM_CHNLS, size,
size))
self.action_space = gym.spaces.Discrete(
GoGame.get_action_size(self.state))
self.group_map = [set(), set()]
self.done = False
def reset(self):
'''
Reset state, go_board, curr_player, prev_player_passed,
done, return state
'''
self.state = GoGame.get_init_board(self.size)
self.group_map = [set(), set()]
self.done = False
return np.copy(self.state)
def step(self, action):
'''
Assumes the correct player is making a move. Black goes first.
return observation, reward, done, info
'''
assert not self.done
if isinstance(action, tuple) or isinstance(action, list) or isinstance(
action, np.ndarray):
assert 0 <= action[0] < self.size
assert 0 <= action[1] < self.size
action = self.size * action[0] + action[1]
elif action is None:
action = self.size**2
actions = np.array([action])
states, group_maps = GoGame.get_batch_next_states(
self.state, actions, self.group_map)
self.state, self.group_map = states[0], group_maps[0]
self.done = GoGame.get_game_ended(self.state)
return np.copy(
self.state), self.get_reward(), self.done, self.get_info()
def game_ended(self):
return self.done
def turn(self):
return GoGame.get_turn(self.state)
def prev_player_passed(self):
return GoGame.get_prev_player_passed(self.state)
def get_valid_moves(self):
return GoGame.get_valid_moves(self.state)
def action_2d_to_1d(self, action_2d):
if action_2d is None:
action_1d = self.size**2
else:
action_1d = action_2d[0] * self.size + action_2d[1]
return action_1d
def uniform_random_action(self):
valid_moves = self.get_valid_moves()
valid_move_idcs = np.argwhere(valid_moves).flatten()
return np.random.choice(valid_move_idcs)
def get_info(self):
"""
:return: Debugging info for the state
"""
return {
'prev_player_passed': GoGame.get_prev_player_passed(self.state),
'turn':
'b' if GoGame.get_turn(self.state) == GoEnv.govars.BLACK else 'w',
'game_ended': GoGame.get_game_ended(self.state)
}
def get_state(self):
"""
:return: copy of state
"""
return np.copy(self.state)
def get_canonical_state(self):
"""
:return: canonical shallow copy of state
"""
return GoGame.get_canonical_form(self.state)
def get_canonical_group_map(self):
if self.turn() == govars.BLACK:
return self.group_map
else:
return list(reversed(self.group_map))
def get_children(self, canonical=False):
"""
:return: Same as get_children, but in canonical form
"""
return GoGame.get_children(self.state, self.group_map, canonical)
def get_winning(self):
"""
:return: Who's currently winning in BLACK's perspective, regardless if the game is over
"""
return GoGame.get_winning(self.state)
def get_winner(self):
"""
Get's the winner in BLACK's perspective
:return:
"""
if self.game_ended():
return self.get_winning()
else:
return 0
def get_reward(self):
'''
Return reward based on reward_method.
heuristic: black total area - white total area
real: 0 for in-game move, 1 for winning, 0 for losing,
0.5 for draw, from black player's perspective.
Winning and losing based on the Area rule
Also known as Trump Taylor Scoring
Area rule definition: https://en.wikipedia.org/wiki/Rules_of_Go#End
'''
if self.reward_method == RewardMethod.REAL:
return self.get_winner()
elif self.reward_method == RewardMethod.HEURISTIC:
black_area, white_area = GoGame.get_areas(self.state)
area_difference = black_area - white_area
if self.game_ended():
return (1 if area_difference > 0 else -1) * self.size**2
return area_difference
else:
raise Exception("Unknown Reward Method")
def __str__(self):
return GoGame.str(self.state)
def close(self):
if hasattr(self, 'window'):
assert hasattr(self, 'pyglet')
self.window.close()
self.pyglet.app.exit()
def render(self, mode='terminal'):
if mode == 'terminal':
print(self.__str__())
elif mode == 'human':
import pyglet
from pyglet.window import mouse
from pyglet.window import key
screen = pyglet.window.get_platform().get_default_display(
).get_default_screen()
window_width = int(min(screen.width, screen.height) * 2 / 3)
window_height = int(window_width * 1.2)
window = pyglet.window.Window(window_width, window_height)
self.window = window
self.pyglet = pyglet
self.user_action = None
# Set Cursor
cursor = window.get_system_mouse_cursor(window.CURSOR_CROSSHAIR)
window.set_mouse_cursor(cursor)
# Outlines
lower_grid_coord = window_width * 0.075
board_size = window_width * 0.85
upper_grid_coord = board_size + lower_grid_coord
delta = board_size / (self.size - 1)
piece_r = delta / 3.3 # radius
@window.event
def on_draw():
pyglet.gl.glClearColor(0.7, 0.5, 0.3, 1)
window.clear()
pyglet.gl.glLineWidth(3)
batch = pyglet.graphics.Batch()
# draw the grid and labels
rendering.draw_grid(batch, delta, self.size, lower_grid_coord,
upper_grid_coord)
# info on top of the board
rendering.draw_info(batch, window_width, window_height,
upper_grid_coord, self.state)
# Inform user what they can do
rendering.draw_command_labels(batch, window_width,
window_height)
rendering.draw_title(batch, window_width, window_height)
batch.draw()
# draw the pieces
rendering.draw_pieces(batch, lower_grid_coord, delta, piece_r,
self.size, self.state)
@window.event
def on_mouse_press(x, y, button, modifiers):
if button == mouse.LEFT:
grid_x = (x - lower_grid_coord)
grid_y = (y - lower_grid_coord)
x_coord = round(grid_x / delta)
y_coord = round(grid_y / delta)
try:
self.window.close()
pyglet.app.exit()
self.user_action = (x_coord, y_coord)
except:
pass
@window.event
def on_key_press(symbol, modifiers):
if symbol == key.P:
self.window.close()
pyglet.app.exit()
self.user_action = None
elif symbol == key.R:
self.reset()
self.window.close()
pyglet.app.exit()
elif symbol == key.E:
self.window.close()
pyglet.app.exit()
self.user_action = -1
pyglet.app.run()
return self.user_action
def __str__(self):
return GoGame.str(self.state)
def get_winning(self):
"""
:return: Who's currently winning in BLACK's perspective, regardless if the game is over
"""
return GoGame.get_winning(self.state)
def get_canonical_state(self):
"""
:return: canonical shallow copy of state
"""
return GoGame.get_canonical_form(self.state)
def game_ended(self):
return GoGame.get_game_ended(self.state)
def prev_player_passed(self):
return GoGame.get_prev_player_passed(self.state)
def get_children(self, canonical=False):
"""
:return: Same as get_children, but in canonical form
"""
return GoGame.get_children(self.state, self.group_map, canonical)
def get_valid_moves(self):
return GoGame.get_valid_moves(self.state)
def get_canonical_state(self):
return GoGame.get_canonical_form(self.state)