def train_by_records(self,
train_epochs,
mini_batch_size=8,
save_file_name='./autojump_rec.npz'):
self.__read_samples()
sample_num = len(self.choice)
self.rewards = self.rewards.reshape((sample_num, 1))
self.dqn = DQN.ZeroGamaDQN(True, self.sample_states.shape[1:])
choice_matrix = np.zeros((sample_num, self.dqn.decision_size))
choice_matrix[[i for i in range(sample_num)], self.choice] = 1
for _ in range(train_epochs):
print('round %d' % _)
mini_batch_index = random.sample([i for i in range(sample_num)],
mini_batch_size)
test_node = self.dqn.train(self.sample_states[mini_batch_index],
choice_matrix[mini_batch_index],
self.rewards[mini_batch_index], 1)
print(test_node)
self.dqn.save_weights(save_file_name)
return
def decide_and_jump(self,
jump_time,
trainable_flag,
save_flag,
weights_file_name=None):
# press_location_change_flag = True
self.press_time = 400
self.getNextState()
resize_state = self.__preprocess_state(self.state)
resize_width = resize_state.shape[0]
resize_height = resize_state.shape[1]
channel = resize_state.shape[2]
self.dqn = DQN.ZeroGamaDQN(trainable_flag,
(resize_height, resize_width, channel),
weights_file_name)
train_flag = False
die_flag = False
last_score = 0
for _ in range(jump_time):
print('trainging_round:', _)
self.getNextState()
### here decide by neural network and basic judge if it's dead, then set the press time
# self.resize_state = imresize(self.state, size=(resize_height, resize_width)).reshape((1, resize_height,
# resize_width, 3))
self.resize_state = self.__preprocess_state(self.state).reshape(
(1, resize_height, resize_width, channel))
if die_flag:
train_flag = False
die_flag = self.__is_died(self.state) ##decide by basic judge
if die_flag:
if trainable_flag and train_flag:
train_degree = 6
# label = np.ones((1, self.dqn.decision_size))
# label = self.last_d_prob + self.last_d_prob[0, self.last_decision]/(self.dqn.decision_size - 1)
label = np.zeros((1, self.dqn.decision_size))
label[0, self.last_decision] = 1
reward = np.array([[-1]])
# label = label/(self.dqn.decision_size - 1)
# label = label / 2
self.dqn.train(self.last_state, label, reward,
train_degree)
self.press_time = 500
# press_location_change_flag = True
else:
### survive, so promote the last decision in last state
cur_score = self.score_recognizer.recognize(self.state)
if trainable_flag and train_flag:
## todo set label and training degree by score change
train_degree = 3
reward = np.array([[(cur_score - last_score) % 10]])
print(reward)
label = np.zeros((1, self.dqn.decision_size))
label[0, self.last_decision] = 1
self.dqn.train(self.last_state, label, reward,
train_degree)
##
self.press_time, self.last_decision, self.last_d_prob = self.dqn.run(
self.resize_state)
self.last_state = self.resize_state
if not train_flag:
train_flag = True
last_score = cur_score
# if press_location_change_flag:
# self.__set_button_position(self.state, die_flag)
# press_location_change_flag = False
self.__set_button_position(self.state, die_flag)
self.__press(die_flag)
print('decision:', self.last_decision, 'press_time:',
self.press_time)
print()
time.sleep(1)
if save_flag:
self.dqn.save_weights('autojump.npz')
return
