Deep Reinforcement learning – 2. 基于tensorflow的DDPG实现

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Deep Reinforcemen learning – 2. 基于tensorflow的DDPG实现

基于我上一篇博客的算法介绍， 使用tensorflow的代码实现，仿真环境使用gym torcs
为了快速训练出结果，我没有使用driver view图像作为输入，而是使用low dimension传感器数据作为输入，
总共29个数据，包括：
– 赛车速度: speedX, speedY, speedZ.
– 赛车在跑道中的位置
– 19个range finder的探测数据：车身与跑道边缘的距离
– 发动机转速
– 车轮速度

输出action有三个维度：
– steer: 方向, 取值范围 [-1,1]
– accel： 油门，取值范围 [0,1]
– brake: 刹车，取值范围 [0,1]

训练1M steps的视频链接：ddpg视频

完整代码的github链接：https://github.com/kennethyu2017/ddpg

下面分模块讲解：

代码框架

再回顾一下ddpg算法的流程图:

actor

actor包含online policy和 target policy 两张神经网络， 其结构是一样的，由于使用low dimension 的数据输入，我没有使用卷积层，
policy网络架构如下 ，取自tensorboard生成的 computation graph：

包括：
– BatchNorm: input batch norm layer
– fully_connected,fully_connected_1: 2个hidden layers
– fully_connected_2: output layer

为了限定policy网络的输出action范围，使用tanh对steer，sigmoid对accelerate和brake，作为bound函数，进行范围限定：

Actor class的主要代码如下：
为了在调试时可以灵活的修改模型架构，policy 网络的模型架构、配置、参数，全部通过在实例化Actor时指定，包括： 对输入数据做归一化的batch norm层、全连接fully-connected层、输出层、
输出bound 函数等。

import tensorflow as tf
from tensorflow.contrib.layers import fully_connected,batch_norm
from common.common import soft_update_online_to_target, copy_online_to_target
DDPG_CFG = tf.app.flags.FLAGS  # alias

class Actor(object):
  def __init__(self, action_dim, online_state_inputs, target_state_inputs,input_normalizer, input_norm_params, n_fc_units, fc_activations, fc_initializers, fc_normalizers, fc_norm_params, fc_regularizers, output_layer_initializer, output_layer_regularizer, output_normalizers, output_norm_params,output_bound_fns, learning_rate, is_training):
    self.a_dim = action_dim
    self.learning_rate = learning_rate
    ...

使用Adam作为gradient descent的算法：

    self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)  # use beta1, beta2 default.

创建online policy网络：

    self._online_action_outputs = self.create_policy_net(scope=DDPG_CFG.online_policy_net_var_scope,
                                                        state_inputs=self.online_state_inputs,
                                                        trainable=True)

    self.online_policy_net_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                                                    scope=DDPG_CFG.online_policy_net_var_scope)
    self.online_policy_net_vars_by_name = {var.name.strip(DDPG_CFG.online_policy_net_var_scope):var
                                           for var in self.online_policy_net_vars}

创建target policy 网络，由于我们采用soft update的方法更新target 网络的参数，所以在创建target网络时指定其参数trainable为False。

    self._target_action_outputs = self.create_policy_net(scope=DDPG_CFG.target_policy_net_var_scope,
                                                        state_inputs=self.target_state_inputs,
                                                        trainable=False)
    self.target_policy_net_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                                    scope=DDPG_CFG.target_policy_net_var_scope)

    self.target_policy_net_vars_by_name = {var.name.strip(DDPG_CFG.target_policy_net_var_scope):var
                                           for var in self.target_policy_net_vars}

策略网络的创建函数 create_policy_net ，根据网络架构参数创建各个layer：

  def create_policy_net(self, state_inputs, scope, trainable):
    with tf.variable_scope(scope):
      #input norm layer
      prev_layer = self.input_normalizer(state_inputs, **self.input_norm_params)

      ##fc layers
      for n_unit, activation, initializer, normalizer, norm_param, regularizer in zip(
          self.n_fc_units, self.fc_activations, self.fc_initializers,
        self.fc_normalizers,self.fc_norm_params, self.fc_regularizers):
        prev_layer = fully_connected(prev_layer, num_outputs=n_unit, activation_fn=activation,
                                     weights_initializer=initializer,
                                     weights_regularizer=regularizer,
                                     normalizer_fn=normalizer,
                                     normalizer_params=norm_param,
                                     biases_initializer=None, #skip bias when use norm.
                                     trainable=trainable)

      ##output layer
      output_layer = fully_connected(prev_layer, num_outputs=self.a_dim, activation_fn=None,
                                     weights_initializer=self.output_layer_initializer,
                                     weights_regularizer=self.output_layer_regularizer,
                                     normalizer_fn=self.output_normalizers,
                                     normalizer_params=self.output_norm_params,
                                     biases_initializer=None, # to skip bias
                                     trainable=trainable)
      ## bound and scale each action dim
      action_unpacked = tf.unstack(output_layer, axis=1)
      action_bounded = []
      for i in range(self.a_dim):
        action_bounded.append(self.output_bound_fns[i](action_unpacked[i]))
      action_outputs = tf.stack(action_bounded, axis=1)

    return action_outputs

policy网络的输出tensor，即action：

  # of online net
  @property
  def online_action_outputs_tensor(self):
    return self._online_action_outputs

  # of target net
  @property
  def target_action_outputs_tensor(self):
    return self._target_action_outputs

定义计算online policy gradient的函数，由于我们只训练online 网络， 所以不用计算target网络的gradient ：

  def compute_online_policy_net_gradients(self, policy_loss):
    grads_and_vars = self.optimizer.compute_gradients(
      policy_loss,var_list=self.online_policy_net_vars)
    grads = [g for (g, _) in grads_and_vars if g is not None]
    compute_op = tf.group(*grads)

    return (grads_and_vars, compute_op)

定义函数，通过Adam optimize将计算好的gradient用于更新online policy网络的参数：

  def apply_online_policy_net_gradients(self, grads_and_vars):
    vars_with_grad = [v for g, v in grads_and_vars if g is not None]
    if not vars_with_grad:
      raise ValueError(
        "$$ ddpg $$ policy net $$ No gradients provided for any variable, check your graph for ops"
        " that do not support gradients,variables %s." %
        ([str(v) for _, v in grads_and_vars]))
    return self.optimizer.apply_gradients(grads_and_vars)

从功能上来讲，上面这两步可以直接合并为使用 optimizer.minimize()函数，我是为了在调试过程中观察gradient的变化情况，所以拆开来实现，
这样可以在调试时取得gradient的tensor，观察其是否收敛。

定义函数，将online policy网络的参数在初始化时copy到target policy 网络，以及在训练时soft update到 target policy网络，
函数返回的是具体操作的op：

  def soft_update_online_to_target(self):
    return soft_update_online_to_target(self.online_q_net_vars_by_name,
                                               self.target_q_net_vars_by_name)

  def copy_online_to_target(self):
    return copy_online_to_target(self.online_q_net_vars_by_name,
                                        self.target_q_net_vars_by_name)

critic

critic包含online q和 target q 两张神经网络， 其结构也是一样的，q网络结构如下，取自tensorboard生成的computation graph：

包括：
– BatchNorm: input batch norm layer
– fully_connected,fully_connected_1: 2个hidden layers
– fully_connected_2: output layer

有两点需要注意：
1、由于q网络需要以action作为输入，且在第二层fully_connected_1 中(代码中通过DDPG_CFG.include_action_fc_layer配置参数指定)
,通过concat包含action输入；
2、对照上面的ddpg流程图，在训练q 网络时，q网络的action输入是采样自replay buffer的action batch， 而在训练 policy网络时，
q网络的action输入是$\mu(s_{i})$, 即policy网络的action输出。我们为q 网络增加一个switch-merge的
控制逻辑–对应上图右下角部分–来同时接入上述两种action输入，并通过cond_training_q tensor控制接通哪一路action输入，
这样我们在训练q网络时，就feed cond_training_q tensor的值为True， 在训练policy网络时，feed cond_training_q tensor的值为False。

Critic class 主要代码如下：同Actor一样，为了在调试时可以灵活的修改模型架构，policy 网络的模型架构、配置、参数，全部通过在实例化Critic
时指定：

class Critic(object):

  def __init__(self, online_state_inputs, target_state_inputs,input_normalizer,input_norm_params, online_action_inputs_training_q, online_action_inputs_training_policy, cond_training_q, target_action_inputs, n_fc_units, fc_activations, fc_initializers, fc_normalizers,fc_norm_params,fc_regularizers, output_layer_initializer,output_layer_regularizer,learning_rate):

    self.online_state_inputs = online_state_inputs
    self.target_state_inputs = target_state_inputs
    ...

同Actor一样 ，也使用Adam作为gradient descent的算法；接着创建online q 和 target q 网络，
其创建函数如下：

  def create_q_net(self, state_inputs, # NHWC format. action_inputs_training_q, scope, trainable, action_inputs_training_policy=None, # None for target net. cond_training_q=None # bool to control switch. can be None for target net. ):
    with tf.variable_scope(scope):
      #input norm layer
      prev_layer=self.input_normalizer(state_inputs,**self.input_norm_params)

      ##fc layers
      l = 1  # start from fc-1 as 1
      for n_unit, activation, initializer, normalizer, norm_param,regularizer in zip(
              self.n_fc_units, self.fc_activations, self.fc_initializers,
              self.fc_normalizers, self.fc_norm_params, self.fc_regularizers):
        # include action_inputs
        if l == DDPG_CFG.include_action_fc_layer:
          if action_inputs_training_policy is None:  # target net
            actions = action_inputs_training_q
          else:  # add logic for selecting online net action inputs
            # switch return :(output_false, output_true)
            (_, sw_action_training_q) = switch(data=action_inputs_training_q,
                                                                   pred=cond_training_q,
                                                                   name='switch_actions_training_q')
            (sw_action_training_policy, _) = switch(data=action_inputs_training_policy,
                                                                        pred=cond_training_q,
                                                                        name='switch_actions_training_policy')
            (actions, _) = merge([sw_action_training_q, sw_action_training_policy])

          prev_layer = tf.concat([prev_layer, actions], axis=1)
        l += 1
        prev_layer = fully_connected(prev_layer, num_outputs=n_unit, activation_fn=activation,
                                     weights_initializer=initializer,
                                     weights_regularizer=regularizer,
                                     normalizer_fn=normalizer, #when specify norm , bias will be ignored.
                                     normalizer_params=norm_param,
                                     trainable=trainable)

      # end fc layers

      ##output layer. fully_connected will create bias which is not wanted in output layer.
      output_layer = fully_connected(inputs=prev_layer,num_outputs=1,
                                     activation_fn=None,
                                     weights_initializer=self.output_layer_initializer,
                                     weights_regularizer=self.output_layer_regularizer,
                                     biases_initializer=None, # to skip bias in output layer
                                    trainable=trainable)

    # == == end with variable_scope() ==
    return output_layer

接着定义gradient的计算、应用函数，以及target q 网络soft update的函数，同Actor类似，不再赘述。

构建总体computation graph

我们知道，tensorflow是基于computation graph进行计算，当我们有了policy 网络、q 网络之后，就可以构架一张大的graph， 把
他们连接起来，构成一个完整的ddpg graph， 进行训练，如下图所示，取自tensorboard的graph显示：

可以看到online policy、online q网络，policy loss\q_loss, 为back propagation添加的gradients\gradients_1 模块，
target policy\ target q网络。

定义构建函数，入参包括actor实例 、 critic实例；两个placeholder：reward_inputs 和 terminated_inputs：在session run时，
通过feed将raplay buffer中采样的reward和terminated batch输入给训练网络；返回训练ddpg时需要运行的op：

def build_ddpg_graph(actor, critic, reward_inputs, terminated_inputs，global_step_tensor):
  #定义label。 对于episode 结束的Transition数据，我们不计算q值， 所以对q值乘上(1.0 - terminated_inputs)
  y_i = reward_inputs + (1.0 - terminated_inputs) * DDPG_CFG.gamma * critic.target_q_outputs_tensor

  #定义q 网络的loss。 对q网络，我们一般采用l2 reg.
  q_reg_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES,scope=DDPG_CFG.online_q_net_var_scope)
  q_loss = tf.add_n([tf.losses.mean_squared_error(labels=y_i, predictions=critic.online_q_outputs_tensor)] + q_reg_loss,
                    name='q_loss')

  #定义policy 网络的loss。 policy网络可以不用reg。
  policy_reg_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope=DDPG_CFG.online_policy_net_var_scope)
  policy_loss =tf.add_n([ -1.0 * tf.reduce_mean(critic.online_q_outputs_tensor)] + policy_reg_loss,
                        name='policy_loss')

  # 定义计算和应用online policy 网络gradient的op， 我们在训练时，运行该op即实现了对policy网络的训练。
  actor_g_and_v, actor_compute_grads_op = actor.compute_online_policy_net_gradients(policy_loss=policy_loss)
  actor_apply_grads_op = actor.apply_online_policy_net_gradients(grads_and_vars=actor_g_and_v)
  train_online_policy_op = actor_apply_grads_op

  # 定义计算和应用online q网络gradient的op， 运行该op即实现了对q网络的训练。
  critic_g_and_v, critic_compute_grads_op = critic.compute_online_q_net_gradients(q_loss=q_loss)
  critic_apply_grads_op = critic.apply_online_q_net_gradients(grads_and_vars=critic_g_and_v)
  train_online_q_op = critic_apply_grads_op

  #定义soft update 的op
  actor_update_target_op = actor.soft_update_online_to_target()
  critic_update_target_op = critic.soft_update_online_to_target()

  #创建control flow的依赖关系
  with tf.control_dependencies([actor_update_target_op, critic_update_target_op]):
    update_target_op = tf.assign_add(global_step_tensor, 1).op  # increment global step

  #copy online -> target，在初始化后进行。
  actor_init_target_op = actor.copy_online_to_target()
  critic_init_target_op = critic.copy_online_to_target()
  copy_online_to_target_op = tf.group(actor_init_target_op, critic_init_target_op)

  # 创建model saver
  saver = tf.train.Saver(keep_checkpoint_every_n_hours=0.5, max_to_keep=5)

  return (copy_online_to_target_op, train_online_policy_op, train_online_q_op, update_target_op, saver)

replay buffer

replay buffer存储我们收集到的transition数据样本，可以通过collections 包中的deque实现。为了反复训练，我增加了将buffer存盘的功能，可以在
下次训练时单独加载buffer文件，不用再一步步执行torcs环境，提高训练效率。

class ReplayBuffer(object):
  def __init__(self, buffer_size, seed, save_segment_size=None, save_path=None):
    #The right side of the deque contains the most recent experiences
    self.buffer_size = buffer_size
    self.buffer = deque([], maxlen=buffer_size)
    if seed is not None:
      np.random.seed(seed)
    self.save=False
    if save_segment_size is not None:
      assert save_path is not None
      self.save = True
      self.save_segment_size = save_segment_size
      self.save_path = save_path
      self.save_data_cnt=0
      self.save_segment_cnt=0

存储transition数据,如果最近新增加的数据达到save_segment_size，就存盘：

  def store(self, transition):
    #deque can take care of max len.
    T = copy.deepcopy(transition)
    self.buffer.append(T)
    if self.save:
      self.save_data_cnt+=1
      if self.save_data_cnt >= self.save_segment_size:
        self.save_segment()
        self.save_data_cnt=0
    del transition

采样mini batch，使用uniform随机采样策略，注意一些边界条件：

  def sample_batch(self, batch_size):
    indices = np.random.permutation(self.length - 1)[:batch_size]
    state_batch, action_batch, reward_batch, next_state_batch, terminated_batch = [], [], [], [], []
    for idx in indices:
      trans_1 = self.buffer[idx]
      if trans_1.terminated is not True:
        # the trans_2 : (a_2, r_2, term_2, s_3)
        trans_2 = self.buffer[idx + 1]
        # we use the data (s_2, a_2, r_2, term_2, s_3)
        state_batch.append(trans_1.next_state)
        action_batch.append(trans_2.action)
        reward_batch.append(trans_2.reward)
        next_state_batch.append(trans_2.next_state)
        terminated_batch.append(trans_2.terminated)
      else:
        ##term_1 is true, so buffer[idx+1] is beginning of new episode,
        # we traverse back.
        if idx != 0:
          trans_0 = self.buffer[idx - 1]  # a_0, r_0, s_1, term_0 = self.buffer[idx - 1]
          if trans_0.terminated is True:  # give up
            continue
          # we use the data (s_1, a_1, r_1, term_1, s_2)
          # s_2 is not used to calc Q cause its terminated. but we still use
          # it to FF through mu_prime/Q_prime then Q*0. guarantee s_2 is accurate formatted.
          state_batch.append(trans_0.next_state)
          action_batch.append(trans_1.action)
          reward_batch.append(trans_1.reward)
          next_state_batch.append(trans_1.next_state)
          terminated_batch.append(trans_1.terminated)
        else:
          # head of buffer, we dont know the previous state , so give up.
          continue

    return (np.array(state_batch), np.array(action_batch), np.array(reward_batch), np.array(next_state_batch),np.array(terminated_batch))

接着定义buffer文件的存储和加载函数,使用pickle包来做deque的序列化存储:

  def save_segment(self):
    self.save_segment_cnt+=1

    data = []
    start = self.length - self.save_segment_size  #always save latest data of segment_size
    end = self.length

    for idx in range(start, end):
      data.append(self.buffer[idx])

    if not os.path.exists(self.save_path):
      os.makedirs(self.save_path)

    abs_file_name = os.path.abspath(os.path.join(self.save_path,
                            '_'.join([DDPG_CFG.replay_buffer_file_name,str(self.save_segment_cnt),time.ctime()])))

    with open(abs_file_name,'wb') as f:
      pkl.dump(data, f)


  def load(self, path):
    #load from file to buffer
    abs_file_pattern = os.path.abspath(os.path.join(path,
                            '_'.join([DDPG_CFG.replay_buffer_file_name,'*'])))
    buffer_files = glob.glob(abs_file_pattern)
    for f_name in buffer_files:
      with open(f_name,'rb') as f:
        data = pkl.load(f)
        self.buffer.extend(data)

train 和 evaluate

接下来，我们可以定义actor、critic的网络架构参数，训练用的hyper-parameter，包括learning rate、l2 regularization ratio、
buffer size等：

policy 网络架构定义,online 和 target 是同样的架构：

import tensorflow as tf
from tensorflow.contrib.layers import variance_scaling_initializer,batch_norm,l2_regularizer

DDPG_CFG = tf.app.flags.FLAGS  # alias
is_training=tf.placeholder(tf.bool, shape=(), name='is_training')

DDPG_CFG.online_policy_net_var_scope = 'online_policy'
DDPG_CFG.target_policy_net_var_scope = 'target_policy'

#因为输入状态参数的scale不统一，我们使用一个batch norm 层，将输入数据的每个维度都
#归一化为均值为0、方差为1：
# -- 1 input norm layers -- 
DDPG_CFG.actor_input_normalizer = batch_norm
DDPG_CFG.actor_input_norm_params =  {
  
  'is_training':is_training,
                                       'data_format':'NHWC',
                                       'updates_collections':None,
                                        'scale':False,  # not gamma. let next fc layer to scale.
                                        'center':True  # beta.
                                        }
# -- 2层fully_connected layers --
DDPG_CFG.actor_n_fc_units = [400, 300]
DDPG_CFG.actor_fc_activations = [tf.nn.elu] * 2
DDPG_CFG.actor_fc_initializers = [variance_scaling_initializer()] * 2
DDPG_CFG.actor_fc_regularizers = [None] * 2
#加一个batch norm层，训练比较稳定
DDPG_CFG.actor_fc_normalizers = [batch_norm] * 2
DDPG_CFG.actor_fc_norm_params =  [{
  
  'is_training':is_training,
                                       'data_format':'NHWC',
                                       'updates_collections':None,
                                       'scale':False,
                                       'center':True
                                         }] *2

# -- 1层 output layer ，由于我们采用tanh和sigmoid函数对输出的action进行了bound，
# 为了避免tanh和sigmoid函数的饱和，在他们之前加一个batch norm层
DDPG_CFG.actor_output_layer_normalizers = batch_norm
DDPG_CFG.actor_output_layer_norm_params = {
  
  'is_training':is_training,
                                       'data_format':'NHWC',
                                       'updates_collections':None,
                                       'scale':False,
                                       'center':False}
DDPG_CFG.actor_output_layer_initializer=tf.random_uniform_initializer(-3e-3,3e-3)

q网络架构定义， online和target是相同的架构：

DDPG_CFG.online_q_net_var_scope = 'online_q'
DDPG_CFG.target_q_net_var_scope = 'target_q'

#-- 1层 input norm layer --
DDPG_CFG.critic_input_normalizer = batch_norm
DDPG_CFG.critic_input_norm_params =  {
  
  'is_training':is_training,
                                       'data_format':'NHWC',
                                       'updates_collections':None,
                                       'scale': False,
                                       'center': True
                                      }

# -- 2层 fully-connected layer,使用l2 regularization --
DDPG_CFG.include_action_fc_layer = 2  # in this layer we include action inputs.
DDPG_CFG.critic_n_fc_units = [400, 300]
DDPG_CFG.critic_fc_activations = [tf.nn.elu] * 2
DDPG_CFG.critic_fc_initializers = [variance_scaling_initializer()] * 2
DDPG_CFG.critic_fc_regularizers = [l2_regularizer(scale=DDPG_CFG.critic_reg_ratio,scope=DDPG_CFG.online_q_net_var_scope)] * 2
DDPG_CFG.critic_fc_normalizers = [batch_norm, None]  # 2nd fc including action input and no BN but has bias.
DDPG_CFG.critic_fc_norm_params =  [{
  
  'is_training':is_training,
                                       'data_format':'NHWC',
                                       'updates_collections':None,
                                       'scale': False,
                                       'center': True
                                    }, None]

# -- 1 output layer --
DDPG_CFG.critic_output_layer_initializer = tf.random_uniform_initializer(-3e-3, 3e-3)

hyper parameter定义：

## hyper-P
DDPG_CFG.actor_learning_rate = 1e-3
DDPG_CFG.critic_learning_rate = 1e-4
DDPG_CFG.critic_reg_ratio = 1e-2
DDPG_CFG.tau = 0.001
DDPG_CFG.gamma = 0.99
DDPG_CFG.num_training_steps = 25*(10**5)  # 2.5M steps total
DDPG_CFG.eval_freq = 3*10000
DDPG_CFG.num_eval_steps = 1000  # eval steps during training
DDPG_CFG.eval_steps_after_training=2000
DDPG_CFG.batch_size = 64
DDPG_CFG.replay_buff_size = 10**6  # 1M
DDPG_CFG.replay_buff_save_segment_size = 30*3000  # every 180,000 Transition data.
DDPG_CFG.greedy_accel_noise_steps = 3*(10**5)

定义train函数, 这个函数也提供在训练后，对得到的具体模型进行evaluation的模式，由入参eval_mode指定：

def train(train_env, agent_action_fn, eval_mode=False):
action_space = train_env.action_space
obs_space = train_env.observation_space
#########实例化 actor,critic, replay buffer#########
# 训练online网络时的state 输入
online_state_inputs = tf.placeholder(tf.float32,
shape=(None, obs_space.shape[0]),
name="online_state_inputs")
# target网络需要的state 输入
target_state_inputs = tf.placeholder(tf.float32,
shape=online_state_inputs.shape,
name="target_state_inputs")
# 训练q 网络时的action输入
online_action_inputs_training_q = tf.placeholder(tf.float32,
shape=(None, action_space.shape[0]),
name='online_action_batch_inputs'
)
# 用于控制q 网络action输入的条件变量：
# True: training q .
# False: training policy.
cond_training_q = tf.placeholder(tf.bool, shape=[], name='cond_training_q')
terminated_inputs = tf.placeholder(tf.float32, shape=(None), name='terminated_inputs')
reward_inputs = tf.placeholder(tf.float32, shape=(None), name='rewards_inputs')
# for summary text
summary_text_tensor = tf.convert_to_tensor(str('summary_text'), preferred_dtype=string)
tf.summary.text(name='summary_text', tensor=summary_text_tensor, collections=[DDPG_CFG.log_summary_keys])
##实例化 actor, critic.
actor = Actor(action_dim=action_space.shape[0],
online_state_inputs=online_state_inputs,
target_state_inputs=target_state_inputs,
input_normalizer=DDPG_CFG.actor_input_normalizer,
input_norm_params=DDPG_CFG.actor_input_norm_params,
n_fc_units=DDPG_CFG.actor_n_fc_units,
fc_activations=DDPG_CFG.actor_fc_activations,
fc_initializers=DDPG_CFG.actor_fc_initializers,
fc_normalizers=DDPG_CFG.actor_fc_normalizers,summary_text_tensor
fc_norm_params=DDPG_CFG.actor_fc_norm_params,
fc_regularizers=DDPG_CFG.actor_fc_regularizers,
output_layer_initializer=DDPG_CFG.actor_output_layer_initializer,
output_layer_regularizer=None,
output_normalizers=DDPG_CFG.actor_output_layer_normalizers,
output_norm_params=DDPG_CFG.actor_output_layer_norm_params,
output_bound_fns=DDPG_CFG.actor_output_bound_fns,
learning_rate=DDPG_CFG.actor_learning_rate,
is_training=is_training)
critic = Critic(online_state_inputs=online_state_inputs,
target_state_inputs=target_state_inputs,
input_normalizer=DDPG_CFG.critic_input_normalizer,
input_norm_params=DDPG_CFG.critic_input_norm_params,
online_action_inputs_training_q=online_action_inputs_training_q,
online_action_inputs_training_policy=actor.online_action_outputs_tensor,
cond_training_q=cond_training_q,
target_action_inputs=actor.target_action_outputs_tensor,
n_fc_units=DDPG_CFG.critic_n_fc_units,
fc_activations=DDPG_CFG.critic_fc_activations,
fc_initializers=DDPG_CFG.critic_fc_initializers,
fc_normalizers=DDPG_CFG.critic_fc_normalizers,
fc_norm_params=DDPG_CFG.critic_fc_norm_params,
fc_regularizers=DDPG_CFG.critic_fc_regularizers,
output_layer_initializer=DDPG_CFG.critic_output_layer_initializer,
output_layer_regularizer = None,
learning_rate=DDPG_CFG.critic_learning_rate)
## track updates.
global_step_tensor = tf.train.create_global_step()
## 构建整个ddpg computation graph 
copy_online_to_target_op, train_online_policy_op, train_online_q_op, update_target_op, saver \
= build_ddpg_graph(actor, critic, reward_inputs, terminated_inputs, global_step_tensor)
#实例化 replay buffer，指定是否将buffer数据保存到文件
replay_buffer = ReplayBuffer(buffer_size=DDPG_CFG.replay_buff_size,
save_segment_size= DDPG_CFG.replay_buff_save_segment_size,
save_path=DDPG_CFG.replay_buffer_file_path,
seed=DDPG_CFG.random_seed
)
#从文件加载buffer数据
if DDPG_CFG.load_replay_buffer_set:
replay_buffer.load(DDPG_CFG.replay_buffer_file_path)
#使用summary监控训练中各项数据、参数的变化，并生成图表，在tensorboard中进行观察，非常方便
sess = tf.Session(graph=tf.get_default_graph())
summary_writer = tf.summary.FileWriter(logdir=os.path.join(DDPG_CFG.log_dir, "train"),
graph=sess.graph)
log_summary_op = tf.summary.merge_all(key=DDPG_CFG.log_summary_keys)
sess.run(fetches=[tf.global_variables_initializer()])
#copy init params from online to target
sess.run(fetches=[copy_online_to_target_op])
#加载之前保存的模型参数checkpoint：
latest_checkpoint = tf.train.latest_checkpoint(DDPG_CFG.checkpoint_dir)
if latest_checkpoint:
tf.logging.info("==== Loading model checkpoint: {}".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
####### start training #########
obs = train_env.reset()  
transition = preprocess_low_dim(obs)
n_episodes = 1
if not eval_mode:
#training 模式
for step in range(1, DDPG_CFG.num_training_steps):
#根据state参数，从online policy网络得到action
policy_out=sess.run(fetches=[actor.online_action_outputs_tensor],
feed_dict={online_state_inputs: transition.next_state[np.newaxis, :], is_training: False})[0]
#通过仿真环境执行action，并保存Transition数据到replay buffer.
# 这一步里会引入action的noise，参见下面的 <noise设计> 段落。
transition=agent_action_fn(policy_out, replay_buffer,train_env)
##从replay buffer采样一个mini-batch
state_batch, action_batch, reward_batch, next_state_batch, terminated_batch = \
replay_buffer.sample_batch(DDPG_CFG.batch_size)
# ---- 1. 训练 policy网络，注意feed的参数 -----------
sess.run(fetches=[train_online_policy_op],
feed_dict = {online_state_inputs: state_batch,
cond_training_q: False,
online_action_inputs_training_q: action_batch,  # feed but not used.
is_training: True
})
# ---- 2. 训练 q 网络 --------------
sess.run(fetches=[train_online_q_op],
feed_dict={ online_state_inputs: state_batch,
cond_training_q: True,
online_action_inputs_training_q: action_batch,
target_state_inputs: next_state_batch,
reward_inputs: reward_batch,
terminated_inputs: terminated_batch,
is_training: True})
# ----- 3. soft update target网络 ---------
sess.run(fetches=[update_target_op], feed_dict=None)
# 每隔 eval_freq steps，我们进行一次evaluation，以便在训练结束后选择好的模型:
if step % DDPG_CFG.eval_freq == 0:
evaluate(env=train_env,
num_eval_steps=DDPG_CFG.num_eval_steps,
preprocess_fn=preprocess_low_dim,
estimate_fn=lambda state: sess.run(fetches=[actor.online_action_outputs_tensor],
feed_dict={online_state_inputs:state,
is_training:False} ),
summary_writer=summary_writer,
saver=saver, sess=sess, global_step=step,
log_summary_op=log_summary_op,summary_text_tensor=summary_text_tensor)
if transition.terminated:
transition = preprocess_low_dim(train_env.reset())
n_episodes +=1
continue  # begin new episode
#evaluate 模式
else: 
evaluate(env=train_env,
num_eval_steps=DDPG_CFG.eval_steps_after_training,
preprocess_fn=preprocess_low_dim,
estimate_fn=lambda state: sess.run(fetches=[actor.online_action_outputs_tensor],
feed_dict={online_state_inputs: state,
is_training: False}),
summary_writer=summary_writer,
saver=None, sess=sess, global_step=0,
log_summary_op=log_summary_op, summary_text_tensor=summary_text_tensor)
sess.close()
train_env.close()

定义evaluate函数如下：

def evaluate(env, num_eval_steps, preprocess_fn, estimate_fn, summary_writer, saver, sess,global_step,log_summary_op,summary_text_tensor):
total_reward = 0
episode_reward = 0
max_episode_reward = 0
n_episodes = 0
n_rewards = 0
terminated = False
transition = preprocess_fn(state=env.reset())
tf.logging.info(' ####### start evaluate @ global step:{}## '.format(global_step))
for estep in range(1,num_eval_steps):      
policy_out = estimate_fn(transition.next_state[np.newaxis,:])
action = policy_output_to_deterministic_action(policy_out,env.action_space)
(state, reward, terminated) = env_step(env, action)
transition = preprocess_fn(state)
# record every reward
total_reward += reward
episode_reward += reward
if reward != 0:
n_rewards += 1  # can represent effective steps in episode
if terminated:
n_episodes += 1
if episode_reward > max_episode_reward:
max_episode_reward = episode_reward
episode_reward = 0            
transition = preprocess_fn(env.reset())
# -- end for estep ---
avg_episode_reward = total_reward / max(1, n_episodes)
avg_episode_steps = n_rewards / max(1, n_episodes)
#训练时我们才保存model参数
saved_name='eval_only_not_save_model'
if saver is not None: 
saved_name = save_model(saver, sess, global_step)
write_summary(summary_writer, global_step, avg_episode_reward, max_episode_reward,
avg_episode_steps,saved_name,sess,log_summary_op,
summary_text_tensor)

我们将evaluation的结果，通过summary保存， 可以通过tensorboard进行查看：

def write_summary(writer, global_step, avg_episode_reward, max_episode_reward, avg_episode_steps,saved_name,sess,log_summary_op, summary_text_tensor):
eval_summary = tf.Summary()  # protocol buffer
eval_summary.value.add(node_name='avg_episode_reward',simple_value=avg_episode_reward, tag="train_eval/avg_episode_reward")
eval_summary.value.add(node_name='max_episode_reward', simple_value=max_episode_reward, tag="train_eval/max_episode_reward")
eval_summary.value.add(node_name='avg_episode_steps', simple_value=avg_episode_steps, tag="train_eval/avg_episode_steps")
writer.add_summary(summary=eval_summary, global_step=global_step)
log_info = 'eval result : global_step:{} avg_episode_reward:{} \ max_episode_reward:{} avg_episode_steps:{} \n saved_file: {} '.format(global_step,
avg_episode_reward,
max_episode_reward,
avg_episode_steps,
saved_name)
log_summary=sess.run(fetches=[log_summary_op],
feed_dict={summary_text_tensor:log_info})
writer.add_summary(summary=log_summary[0], global_step=global_step)
writer.flush()

通过saver对象保存模型参数：

def save_model(saver, sess,global_step):
# save model. will save both online and target networks.
return saver.save(sess=sess, save_path=DDPG_CFG.checkpoint_dir, global_step=global_step)

接下来，我们就可以创建torcs环境，开始训练：
注意：为了加速训练，torcs使用text模式(-T选项) ，即没有画面显示。你可以更改下面的vision选项打开画面显示。
当你想选择赛道时，在shell里通过运行 sudo torcs ， 进入 practice->configure race 界面进行配置，再退出，然后开始训练。

if __name__ == "__main__":
tf.logging.info("@@@ start ddpg training gym_torcs @@@ start time:{}".format(time.ctime()))
# Generate a Torcs environment
env_train = torcs_env_wrapper(vision=False, throttle=True, gear_change=False,port=3101)
train(env_train,agent_action,eval_mode=False)

gym torcs的reward设计和episode终结条件：

我们定义reward和episode终结条件如下：

- 每一步action执行时，汽车的当前速度投影到赛道正前方向的值,参照  [yanpanlau的描述](https://github.com/yanpanlau/DDPG-Keras-Torcs)：

- 当汽车与周围发生碰撞时， reward=-1 作为惩罚，但并不结束当前episode
- 当汽车在持续200步进展缓慢(前后两步的距离差小于0.05米)时，给出reward=-1作为惩罚，并结束当前episode
- 汽车如果掉头行驶，则结束当前episode

这样设计的目的也是了实现exploration，即让汽车尽量多的往前跑，去覆盖到赛道的各个部分，并且避免产生大量低速行驶的数据。

具体实现参见本项目中的gym_torcs.py文件

noise的设计

noise的引入是为了exploration，ddpg paper中使用的是OU noise，但经过我实践，标准的OU noise应用在torcs这种环境效果并不算好，
我参考了yanpanlau 的做法， 即在训练一开始，引入大量直线加速的noise， 然后随着训练步骤衰减该noise，
这样做的效果很明显，就是让模型快速学习到高速行驶状态(车速也是状态输入的一部分)下的q网络和policy网络参数，
而且在速度的保证下，可以往前开的尽量的远，探索到更多赛段。你可以探索更多有效的noise，去帮助ddpg寻找最优解。
我管这种noise 叫做greedy accelerate:

def greedy_function(x, mu, theta, sigma):
return theta * (mu - x) + sigma * np.random.randn()
epsilon=1
def policy_output_to_stochastic_action(output, action_space):
global epsilon
output = np.squeeze(output, axis=0)
epsilon -= 1.0 / DDPG_CFG.greedy_accel_noise_steps
greedy_noise=np.array( [max(epsilon, 0) * greedy_function(output[0], 0.0, 0.60, 0.30),  # steer
max(epsilon, 0) * greedy_function(output[1], 0.5, 1.00, 0.10),  # accel
max(epsilon, 0) * greedy_function(output[2], -0.1, 1.00, 0.05)]) # brake
stochastic_action = greedy_noise + output
bounded = np.clip(stochastic_action, action_space.low, action_space.high)
return bounded

训练结果

使用机器配置：cpu i5-3450, gpu Nvidia 1050Ti, 训练1M steps 耗时 13小时，
基本可以得到一个跑分（evo-3-l赛道，Alpine赛道）在10k/episode 以上的模型。
ddpg paper中的模型训练2.5M steps后，得到的 torcs最好跑分为1840/episode， 当然paper中的模型是为了
通用(也用于atari、mujuco等仿真环境)， 对于torcs没有特别的处理。
我得到的模型应该是有overfit torcs具体某一条赛道的成分存在。
观察 q_loss的曲线，是往下收敛的:

观察policy loss 曲线，虽然没有往下收敛，基本比较平稳，需要持续的训练才能明显的收敛。我们可以取一个较好的中间结果，也可以看到赛车可以30km左右的速度顺利过弯了。

再看一下training过程中evaluate的结果，可以看到中间有些步骤的跑分可以达到10k，由于我们每次evaluation都保存了模型参数，我们 可以取出对应的模型:

上述1M steps训练后得到的不算是最优模型：
– 不能稳定跑完整条赛道，只能跑完前面一段
– 速度40km上下，还不能尽力加速，
– 多赛道的表现不好，因为我只在一条赛道上进行训练， 有overfit存在。

我们验证了ddpg算法的正确性。
ddpg的训练过程存在很大的不稳定性，后期的训练过程很可能覆盖前期训练的结果，所以我们需要大量的反复训练，然后在过程中
寻找一个相对最优解。
如果你想自己动手训练一下，我建议两点：

    -- 加上一些基本的Deep learning 技巧去调试：比如cross validation寻找最优hyper parameters,
early stopping等。
-- 算法上，更新Deepmind 2017年发表的Rainbow