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Python 还能实现哪些 AI 游戏？附上代码一起来一把( 二 )

PythonAI 游戏

2、行动决策机制：
首先在程序框架中设定不同的行动作为训练对象
# 行动paddle_1(训练对象)if action[0] == 1:self.paddle_1_speed = 0elif action[1] == 1:self.paddle_1_speed = -self.paddleSpeedelif action[2] == 1:self.paddle_1_speed = self.paddleSpeedself.paddle_1_pos = self.paddle_1_pos[0], max(min(self.paddle_1_speed + self.paddle_1_pos[1], 420), 10)接着设置一个简单的初始化决策 。根据结果判断奖励和惩罚机制，即球撞到拍上奖励，撞到墙上等等惩罚：
其中代码如下：
【Python 还能实现哪些 AI 游戏？附上代码一起来一把】# 行动paddle_2(设置一个简单的算法使paddle_2的表现较优, 非训练对象)if self.ball_pos[0] >= 305.:if not self.paddle_2_pos[1] == self.ball_pos[1] + 7.5:if self.paddle_2_pos[1] < self.ball_pos[1] + 7.5:self.paddle_2_speed = self.paddleSpeedself.paddle_2_pos = self.paddle_2_pos[0], max(min(self.paddle_2_pos[1] + self.paddle_2_speed, 420), 10)if self.paddle_2_pos[1] > self.ball_pos[1] - 42.5:self.paddle_2_speed = -self.paddleSpeedself.paddle_2_pos = self.paddle_2_pos[0], max(min(self.paddle_2_pos[1] + self.paddle_2_speed, 420), 10)else:self.paddle_2_pos = self.paddle_2_pos[0], max(min(self.paddle_2_pos[1] + 7.5, 420), 10)# 行动ball# 球撞拍上if self.ball_pos[0] <= self.paddle_1_pos[0] + 10.:if self.ball_pos[1] + 7.5 >= self.paddle_1_pos[1] and self.ball_pos[1] <= self.paddle_1_pos[1] + 42.5:self.ball_pos = 20., self.ball_pos[1]self.ballSpeed = -self.ballSpeed[0], self.ballSpeed[1]reward = self.hitRewardif self.ball_pos[0] + 15 >= self.paddle_2_pos[0]:if self.ball_pos[1] + 7.5 >= self.paddle_2_pos[1] and self.ball_pos[1] <= self.paddle_2_pos[1] + 42.5:self.ball_pos = 605., self.ball_pos[1]self.ballSpeed = -self.ballSpeed[0], self.ballSpeed[1]# 拍未接到球(另外一个拍得分)if self.ball_pos[0] < 5.:self.paddle_2_score += 1reward = self.loseRewardself.__resetelif self.ball_pos[0] > 620.:self.paddle_1_score += 1reward = self.winRewardself.__reset# 球撞墙上if self.ball_pos[1] <= 10.:self.ballSpeed = self.ballSpeed[0], -self.ballSpeed[1]self.ball_pos = self.ball_pos[0], 10elif self.ball_pos[1] >= 455:self.ballSpeed = self.ballSpeed[0], -self.ballSpeed[1]self.ball_pos = self.ball_pos[0], 4553、DQN算法搭建：
为了方便整体算法的调用，我们首先定义神经网络的函数，包括卷积层损失等函数定义具体如下可见：
'''获得初始化weight权重'''def init_weight_variable(self, shape):return tf.Variable(tf.truncated_normal(shape, stddev=0.01))'''获得初始化bias权重'''def init_bias_variable(self, shape):return tf.Variable(tf.constant(0.01, shape=shape))'''卷积层'''def conv2D(self, x, W, stride):return tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding="SAME")'''池化层'''def maxpool(self, x):return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')'''计算损失'''def compute_loss(self, q_values, action_now, target_q_values):tmp = tf.reduce_sum(tf.multiply(q_values, action_now), reduction_indices=1)loss = tf.reduce_mean(tf.square(target_q_values - tmp))return loss'''下一帧'''def next_frame(self, action_now, scene_now, gameState):x_now, reward, terminal = gameState.update_frame(action_now)x_now = cv2.cvtColor(cv2.resize(x_now, (80, 80)), cv2.COLOR_BGR2GRAY)_, x_now = cv2.threshold(x_now, 127, 255, cv2.THRESH_BINARY)x_now = np.reshape(x_now, (80, 80, 1))scene_next = np.Append(x_now, scene_now[:, :, 0:3], axis=2)return scene_next, reward, terminal'''计算target_q_values'''def compute_target_q_values(self, reward_batch, q_values_batch, minibatch):target_q_values =for i in range(len(minibatch)):if minibatch[i][4]:target_q_values.append(reward_batch[i])else:target_q_values.append(reward_batch[i] + self.gamma * np.max(q_values_batch[i]))return target_q_values然后定义整体的类变量DQN，分别定义初始化和训练函数，其中网络层哪里主要就是神经网络层的调用 。然后在训练函数里面记录当前动作和数据加载入优化器中达到模型训练效果 。
其中代码如下：
def __init__(self, options):self.options = optionsself.num_action = options['num_action']self.lr = options['lr']self.modelDir = options['modelDir']self.init_prob = options['init_prob']self.end_prob = options['end_prob']self.OBSERVE = options['OBSERVE']self.EXPLORE = options['EXPLORE']self.action_interval = options['action_interval']self.REPLAY_MEMORY = options['REPLAY_MEMORY']self.gamma = options['gamma']self.batch_size = options['batch_size']self.save_interval = options['save_interval']self.logfile = options['logfile']self.is_train = options['is_train']'''训练网络'''def train(self, session):x, q_values_ph = self.create_networkaction_now_ph = tf.placeholder('float', [None, self.num_action])target_q_values_ph = tf.placeholder('float', [None])# 计算lossloss = self.compute_loss(q_values_ph, action_now_ph, target_q_values_ph)# 优化目标trainStep = tf.train.AdamOptimizer(self.lr).minimize(loss)# 游戏gameState = PongGame# 用于记录数据dataDeque = deque# 当前的动作action_now = np.zeros(self.num_action)action_now[0] = 1# 初始化游戏状态x_now, reward, terminal = gameState.update_frame(action_now)x_now = cv2.cvtColor(cv2.resize(x_now, (80, 80)), cv2.COLOR_BGR2GRAY)_, x_now = cv2.threshold(x_now, 127, 255, cv2.THRESH_BINARY)scene_now = np.stack((x_now, )*4, axis=2)# 读取和保存checkpointsaver = tf.train.Saversession.run(tf.global_variables_initializer)checkpoint = tf.train.get_checkpoint_state(self.modelDir)if checkpoint and checkpoint.model_checkpoint_path:saver.restore(session, checkpoint.model_checkpoint_path)print('[INFO]: Load %s successfully...' % checkpoint.model_checkpoint_path)else:print('[INFO]: No weights found, start to train a new model...')prob = self.init_probnum_frame = 0logF = open(self.logfile, 'a')while True:q_values = q_values_ph.eval(feed_dict={x: [scene_now]})action_idx = get_action_idx(q_values=q_values,prob=prob,num_frame=num_frame,OBSERVE=self.OBSERVE,num_action=self.num_action)action_now = np.zeros(self.num_action)action_now[action_idx] = 1prob = down_prob(prob=prob,num_frame=num_frame,OBSERVE=self.OBSERVE,EXPLORE=self.EXPLORE,init_prob=self.init_prob,end_prob=self.end_prob)for _ in range(self.action_interval):scene_next, reward, terminal = self.next_frame(action_now=action_now,scene_now=scene_now, gameState=gameState)scene_now = scene_nextdataDeque.append((scene_now, action_now, reward, scene_next, terminal))if len(dataDeque) > self.REPLAY_MEMORY:dataDeque.popleftloss_now = Noneif (num_frame > self.OBSERVE):minibatch = random.sample(dataDeque, self.batch_size)scene_now_batch = [mb[0] for mb in minibatch]action_batch = [mb[1] for mb in minibatch]reward_batch = [mb[2] for mb in minibatch]scene_next_batch = [mb[3] for mb in minibatch]q_values_batch = q_values_ph.eval(feed_dict={x: scene_next_batch})target_q_values = self.compute_target_q_values(reward_batch, q_values_batch, minibatch)trainStep.run(feed_dict={target_q_values_ph: target_q_values,action_now_ph: action_batch,x: scene_now_batch})loss_now = session.run(loss, feed_dict={target_q_values_ph: target_q_values,action_now_ph: action_batch,x: scene_now_batch})num_frame += 1if num_frame % self.save_interval == 0:name = 'DQN_Pong'saver.save(session, os.path.join(self.modelDir, name), global_step=num_frame)log_content = '<Frame>: %s, <Prob>: %s, <Action>: %s, <Reward>: %s, <Q_max>: %s, <Loss>: %s' % (str(num_frame), str(prob), str(action_idx), str(reward), str(np.max(q_values)), str(loss_now))logF.write(log_content + 'n')print(log_content)logF.close'''创建网络'''def create_network(self):'''W_conv1 = self.init_weight_variable([9, 9, 4, 16])b_conv1 = self.init_bias_variable([16])W_conv2 = self.init_weight_variable([7, 7, 16, 32])b_conv2 = self.init_bias_variable([32])W_conv3 = self.init_weight_variable([5, 5, 32, 32])b_conv3 = self.init_bias_variable([32])W_conv4 = self.init_weight_variable([5, 5, 32, 64])b_conv4 = self.init_bias_variable([64])W_conv5 = self.init_weight_variable([3, 3, 64, 64])b_conv5 = self.init_bias_variable([64])'''W_conv1 = self.init_weight_variable([8, 8, 4, 32])b_conv1 = self.init_bias_variable([32])W_conv2 = self.init_weight_variable([4, 4, 32, 64])b_conv2 = self.init_bias_variable([64])W_conv3 = self.init_weight_variable([3, 3, 64, 64])b_conv3 = self.init_bias_variable([64])# 5 * 5 * 64 = 1600W_fc1 = self.init_weight_variable([1600, 512])b_fc1 = self.init_bias_variable([512])W_fc2 = self.init_weight_variable([512, self.num_action])b_fc2 = self.init_bias_variable([self.num_action])# input placeholderx = tf.placeholder('float', [None, 80, 80, 4])'''conv1 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(x, W_conv1, 4) + b_conv1, training=self.is_train, momentum=0.9))conv2 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv1, W_conv2, 2) + b_conv2, training=self.is_train, momentum=0.9))conv3 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv2, W_conv3, 2) + b_conv3, training=self.is_train, momentum=0.9))conv4 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv3, W_conv4, 1) + b_conv4, training=self.is_train, momentum=0.9))conv5 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv4, W_conv5, 1) + b_conv5, training=self.is_train, momentum=0.9))flatten = tf.reshape(conv5, [-1, 1600])'''conv1 = tf.nn.relu(self.conv2D(x, W_conv1, 4) + b_conv1)pool1 = self.maxpool(conv1)conv2 = tf.nn.relu(self.conv2D(pool1, W_conv2, 2) + b_conv2)conv3 = tf.nn.relu(self.conv2D(conv2, W_conv3, 1) + b_conv3)flatten = tf.reshape(conv3, [-1, 1600])fc1 = tf.nn.relu(tf.layers.batch_normalization(tf.matmul(flatten, W_fc1) + b_fc1, training=self.is_train, momentum=0.9))fc2 = tf.matmul(fc1, W_fc2) + b_fc2return x, fc2


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