def expand_pocliy_random(board: Board):
board_array = board.get_board_array()
conv_available = _convolve_board_available_narrow(board_array)
cnt = conv_available.sum()
probability = conv_available / cnt
return_list = []
for move in board.available:
if conv_available[move]:
return_list.append((move, probability[move]))
return return_list
def simulate_network(board: Board, limit=100, q_confidence=0.5):
random_bound = 4
net_run_time = 0.
start_time = time.time() * 1000
is_end, winner = False, None
for i in range(limit):
is_end, winner = board.check_winner()
if is_end:
# print(i, 'end')
break
must = board.check_must()
if must is not None:
if not board.play(must):
raise ValueError('Must Error')
continue
t1 = time.time() * 1000
action_prob = rollout_policy_network(board)
# action_prob = policy(board)
t2 = time.time() * 1000
net_run_time += t2 - t1
if i < random_bound:
next_action = np.random.choice(15 * 15, 1, p=action_prob)[0]
while not board.play(next_action):
next_action = np.random.choice(15 * 15, 1, p=action_prob)[0]
else:
next_action = max(board.available,
key=lambda move: action_prob[move])
board.play(next_action)
end_time = time.time() * 1000
# print('%.5fms %.5fms %.5f%%' % (end_time - start_time, net_run_time, net_run_time * 100 / (end_time - start_time)))
if is_end:
if winner is not None:
return winner
value_network_Q = ValueRunner(board.get_board_array())
if board.current_player == BLACK_: # array shape (white, black)
value_network_Q = 1 - value_network_Q # change to black side
return q_confidence * (value_network_Q - 0.5) + 0.5
def expand_policy_network(board: Board) -> List[Tuple[int, float]]:
board_array = board.get_board_array()
probability = TreePolicyRunner(board_array)
if len(board.moved) == 1:
if board.moved[0] == move_xy2int(7, 7):
return list(map(lambda move: (move, probability[move]), __second_move_available))
if len(board.must[board.current_player]):
return list(map(lambda move: (move, probability[move]), (board.must[board.current_player])))
if len(board.must[0] | board.must[1]):
return list(map(lambda move: (move, probability[move]), (board.must[0] | board.must[1])))
conv_available = _convolve_board_available_wide(board_array)
return_list = []
for move in board.available:
if conv_available[move]:
return_list.append((move, probability[move]))
return return_list
def rollout_policy_network(board: Board):
probability = RolloutPolicyRunner(board.get_board_array())
return probability
def rollout_policy_random(board: Board):
board_array = board.get_board_array()
conv_available = _convolve_board_available_narrow(board_array)
probability = np.random.rand(15 * 15) * conv_available
return probability