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 simulate_random(board: Board, limit=200):
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 board.play(must):
# continue
if not board.play(must):
raise ValueError('Must Error')
# board.show()
continue
t1 = time.time() * 1000
action_prob = rollout_policy_random(board)
# action_prob = policy(board)
t2 = time.time() * 1000
net_run_time += t2 - t1
next_action = np.argmax(action_prob)
while not board.play(next_action):
next_action = np.argmax(action_prob)
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
return 0.5