Ubuntu安装Pyenv+Anaconda2+PyCharm+OpenAI-gym+Cuda+TensorFlow （四）

八. 测试

1. 测试Cuda
　　该部分主要参考南墙已破简书中的cuda测试内容
　　在/home/gh目录下下载cuda测试集源代码：

$ cuda-install-samples-8.0.sh ~

　　进入到这个文件夹进行编译：

$ cd ~/NVIDIA_CUDA-8.0_Samples
$ make

　　编译完成后运行deviceQuery.py和bandwidthTest.py两个脚本：

可以看到已经检测到显卡Device 0: “GeForce GT 650m”，并且最后一行运行结果Result = PASS。
　　这里我刚安完cuda和cuDNN之后进行测试同样遇到了脚本运行失败的结果，关于这个问题博主南墙已破给出了重启的解决方案，我是直接注销了下，然后再回来也行。

2. 测试TensorFlow和gym
　　打开PyCharm，新建一个叫做test的工程，python解释器选择anaconda下带有tensorflow环境的那个：

　　在PyCharm编辑栏File中选择Settings——Project:test——Project Interpreter，在右侧可以看到在这个python解释器下已经安装了的所有功能包，我们可以看到gym和tensorflow都已经安装在了这个tensorflow的虚拟环境中，此外还有很多其它常用的功能包。

至此，可以确定gym和tensorflow安装无误。

3. 跑一个DQN的例子
　　该部分代码全部来自于知乎专栏—智能单元中Flood Sung的150行代码实现DQN算法，这个专栏对深度学习和增强学习有不少不错的科普贴，这里安利一下：P
　　在上面的新建工程test下，新建一个python脚本dqn，复制如下代码：

#!/usr/bin/env python    # encoding: utf-8  """     @author: GH    @contact: [e-mail]     @file: dqn.py     @time: 16-12-28 下午3:32     """import gymimport tensorflow as tfimport numpy as npimport randomfrom collections import deque# Hyper Parameters for DQNGAMMA = 0.9 # discount factor for target QINITIAL_EPSILON = 0.5 # starting value of epsilonFINAL_EPSILON = 0.01 # final value of epsilonREPLAY_SIZE = 10000 # experience replay buffer sizeBATCH_SIZE = 32 # size of minibatchclass DQN():  # DQN Agent  def __init__(self, env):    # init experience replay    self.replay_buffer = deque()    # init some parameters    self.time_step = 0    self.epsilon = INITIAL_EPSILON    self.state_dim = env.observation_space.shape[0]    self.action_dim = env.action_space.n    self.create_Q_network()    self.create_training_method()    # Init session    self.session = tf.InteractiveSession()    self.session.run(tf.initialize_all_variables())  def create_Q_network(self):    # network weights    W1 = self.weight_variable([self.state_dim,20])    b1 = self.bias_variable([20])    W2 = self.weight_variable([20,self.action_dim])    b2 = self.bias_variable([self.action_dim])    # input layer    self.state_input = tf.placeholder("float",[None,self.state_dim])    # hidden layers    h_layer = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)    # Q Value layer    self.Q_value = tf.matmul(h_layer,W2) + b2  def create_training_method(self):    self.action_input = tf.placeholder("float",[None,self.action_dim]) # one hot presentation    self.y_input = tf.placeholder("float",[None])    Q_action = tf.reduce_sum(tf.mul(self.Q_value,self.action_input),reduction_indices = 1)    self.cost = tf.reduce_mean(tf.square(self.y_input - Q_action))    self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost)  def perceive(self,state,action,reward,next_state,done):    one_hot_action = np.zeros(self.action_dim)    one_hot_action[action] = 1    self.replay_buffer.append((state,one_hot_action,reward,next_state,done))    if len(self.replay_buffer) > REPLAY_SIZE:      self.replay_buffer.popleft()    if len(self.replay_buffer) > BATCH_SIZE:      self.train_Q_network()  def train_Q_network(self):    self.time_step += 1    # Step 1: obtain random minibatch from replay memory    minibatch = random.sample(self.replay_buffer,BATCH_SIZE)    state_batch = [data[0] for data in minibatch]    action_batch = [data[1] for data in minibatch]    reward_batch = [data[2] for data in minibatch]    next_state_batch = [data[3] for data in minibatch]    # Step 2: calculate y    y_batch = []    Q_value_batch = self.Q_value.eval(feed_dict={self.state_input:next_state_batch})    for i in range(0,BATCH_SIZE):      done = minibatch[i][4]      if done:        y_batch.append(reward_batch[i])      else :        y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i]))    self.optimizer.run(feed_dict={      self.y_input:y_batch,      self.action_input:action_batch,      self.state_input:state_batch      })  def egreedy_action(self,state):    Q_value = self.Q_value.eval(feed_dict = {      self.state_input:[state]      })[0]    if random.random() <= self.epsilon:      return random.randint(0,self.action_dim - 1)    else:      return np.argmax(Q_value)    self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON)/10000  def action(self,state):    return np.argmax(self.Q_value.eval(feed_dict = {      self.state_input:[state]      })[0])  def weight_variable(self,shape):    initial = tf.truncated_normal(shape)    return tf.Variable(initial)  def bias_variable(self,shape):    initial = tf.constant(0.01, shape = shape)    return tf.Variable(initial)# ---------------------------------------------------------# Hyper ParametersENV_NAME = 'CartPole-v0'EPISODE = 10000 # Episode limitationSTEP = 300 # Step limitation in an episodeTEST = 10 # The number of experiment test every 100 episodedef main():  # initialize OpenAI Gym env and dqn agent  env = gym.make(ENV_NAME)  agent = DQN(env)  for episode in xrange(EPISODE):    # initialize task    state = env.reset()    # Train    for step in xrange(STEP):      action = agent.egreedy_action(state) # e-greedy action for train      next_state,reward,done,_ = env.step(action)      # Define reward for agent      reward_agent = -1 if done else 0.1      agent.perceive(state,action,reward,next_state,done)      state = next_state      if done:        break    # Test every 100 episodes    if episode % 100 == 0:      total_reward = 0      for i in xrange(TEST):        state = env.reset()        for j in xrange(STEP):          env.render()          action = agent.action(state) # direct action for test          state,reward,done,_ = env.step(action)          total_reward += reward          if done:            break      ave_reward = total_reward/TEST      print 'episode: ',episode,'Evaluation Average Reward:',ave_reward      if ave_reward == 200:        breakif __name__ == '__main__':  main()

　　在PyCharm编辑栏Run条目下选择Edit Configuration，定义一个name，Scrips选择刚才新建的dqn.py，点击OK确定。
　　现在按下编辑栏上绿色小三角按钮就可以运行啦！

到此为止整个tensorflow+gym的框架就搭好啦，之后就可以自己写算法利用gym平台进行测试啦。

全文参考：
[1]. 南墙已破的简书http://www.jianshu.com/p/c89b97d052b7
[2]. TensorFlow官网https://www.tensorflow.org/get_started/os_setup#anaconda_installation
[3]. OpenAI-gym官网https://gym.openai.com/docs
[4]. 知乎专栏-智能单元https://zhuanlan.zhihu.com/intelligentunit
[5]. super的博客园http://www.cnblogs.com/super-d2/p/4725818.html

个人理解，如有错误请指出

　　　　　　　

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