code: import time
  from collections import deque
  
  import gym
  import numpy as np
  import tensorflow as tf
  
  from stable_baselines import logger
  from stable_baselines.common import explained_variance, tf_util, ActorCriticRLModel, SetVerbosity, TensorboardWriter
  from stable_baselines.common.policies import ActorCriticPolicy, RecurrentActorCriticPolicy
  from stable_baselines.common.runners import AbstractEnvRunner
  from stable_baselines.a2c.utils import discount_with_dones, Scheduler, find_trainable_variables, mse, \
      total_episode_reward_logger
  from stable_baselines.ppo2.ppo2 import safe_mean
  
  class A2C(ActorCriticRLModel):
      """
      The A2C (Advantage Actor Critic) model class, https://arxiv.org/abs/1602.01783
  
      :param policy: (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, ...)
      :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
      :param gamma: (float) Discount factor
      :param n_steps: (int) The number of steps to run for each environment per update
          (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
      :param vf_coef: (float) Value function coefficient for the loss calculation
      :param ent_coef: (float) Entropy coefficient for the loss caculation
      :param max_grad_norm: (float) The maximum value for the gradient clipping
      :param learning_rate: (float) The learning rate
      :param alpha: (float)  RMSProp decay parameter (default: 0.99)
      :param epsilon: (float) RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update)
          (default: 1e-5)
      :param lr_schedule: (str) The type of scheduler for the learning rate update ('linear', 'constant',
                                'double_linear_con', 'middle_drop' or 'double_middle_drop')
      :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
      :param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
      :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
                                (used only for loading)
      :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
      :param full_tensorboard_log: (bool) enable additional logging when using tensorboard
          WARNING: this logging can take a lot of space quickly
      """
  
      def __init__(self, policy, env, gamma=0.99, n_steps=5, vf_coef=0.25, ent_coef=0.01, max_grad_norm=0.5,
                   learning_rate=7e-4, alpha=0.99, epsilon=1e-5, lr_schedule='constant', verbose=0, tensorboard_log=None,
                   _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False):
  
          super(A2C, self).__init__(policy=policy, env=env, verbose=verbose, requires_vec_env=True,
                                    _init_setup_model=_init_setup_model, policy_kwargs=policy_kwargs)
  
          self.n_steps = n_steps
          self.gamma = gamma
          self.vf_coef = vf_coef
          self.ent_coef = ent_coef
          self.max_grad_norm = max_grad_norm
          self.alpha = alpha
          self.epsilon = epsilon
          self.lr_schedule = lr_schedule
          self.learning_rate = learning_rate
          self.tensorboard_log = tensorboard_log
          self.full_tensorboard_log = full_tensorboard_log
  
          self.graph = None
          self.sess = None
          self.learning_rate_ph = None
          self.n_batch = None
          self.actions_ph = None
          self.advs_ph = None
          self.rewards_ph = None
          self.pg_loss = None
          self.vf_loss = None
          self.entropy = None
          self.params = None
          self.apply_backprop = None
          self.train_model = None
          self.step_model = None
          self.step = None
          self.proba_step = None
          self.value = None
          self.initial_state = None
          self.learning_rate_schedule = None
          self.summary = None
          self.episode_reward = None
  
          # if we are loading, it is possible the environment is not known, however the obs and action space are known
          if _init_setup_model:
              self.setup_model()
  
      def _get_pretrain_placeholders(self):
          policy = self.train_model
          if isinstance(self.action_space, gym.spaces.Discrete):
              return policy.obs_ph, self.actions_ph, policy.policy
          return policy.obs_ph, self.actions_ph, policy.deterministic_action
  
      def setup_model(self):
          with SetVerbosity(self.verbose):
  
              assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the A2C model must be an " \
                                                                  "instance of common.policies.ActorCriticPolicy."
  
              self.graph = tf.Graph()
              with self.graph.as_default():
                  self.sess = tf_util.make_session(graph=self.graph)
  
                  self.n_batch = self.n_envs * self.n_steps
  
                  n_batch_step = None
                  n_batch_train = None
                  if issubclass(self.policy, RecurrentActorCriticPolicy):
                      n_batch_step = self.n_envs
                      n_batch_train = self.n_envs * self.n_steps
  
                  step_model = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
                                           n_batch_step, reuse=False, **self.policy_kwargs)
  
                  with tf.variable_scope("train_model", reuse=True,
                                         custom_getter=tf_util.outer_scope_getter("train_model")):
                      train_model = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs,
                                                self.n_steps, n_batch_train, reuse=True, **self.policy_kwargs)
  
                  with tf.variable_scope("loss", reuse=False):
                      self.actions_ph = train_model.pdtype.sample_placeholder([None], name="action_ph")
                      self.advs_ph = tf.placeholder(tf.float32, [None], name="advs_ph")
                      self.rewards_ph = tf.placeholder(tf.float32, [None], name="rewards_ph")
                      self.learning_rate_ph = tf.placeholder(tf.float32, [], name="learning_rate_ph")
  
                      neglogpac = train_model.proba_distribution.neglogp(self.actions_ph)
                      self.entropy = tf.reduce_mean(train_model.proba_distribution.entropy())
                      self.pg_loss = tf.reduce_mean(self.advs_ph * neglogpac)
                      self.vf_loss = mse(tf.squeeze(train_model.value_flat), self.rewards_ph)
                      # https://arxiv.org/pdf/1708.04782.pdf#page=9, https://arxiv.org/pdf/1602.01783.pdf#page=4
                      # and https://github.com/dennybritz/reinforcement-learning/issues/34
                      # suggest to add an entropy component in order to improve exploration.
                      loss = self.pg_loss - self.entropy * self.ent_coef + self.vf_loss * self.vf_coef
  
                      tf.summary.scalar('entropy_loss', self.entropy)
                      tf.summary.scalar('policy_gradient_loss', self.pg_loss)
                      tf.summary.scalar('value_function_loss', self.vf_loss)
                      tf.summary.scalar('loss', loss)
  
                      self.params = find_trainable_variables("model")
                      grads = tf.gradients(loss, self.params)
                      if self.max_grad_norm is not None:
                          grads, _ = tf.clip_by_global_norm(grads, self.max_grad_norm)
                      grads = list(zip(grads, self.params))
  
                  with tf.variable_scope("input_info", reuse=False):
                      tf.summary.scalar('discounted_rewards', tf.reduce_mean(self.rewards_ph))
                      tf.summary.scalar('learning_rate', tf.reduce_mean(self.learning_rate))
                      tf.summary.scalar('advantage', tf.reduce_mean(self.advs_ph))
                      if self.full_tensorboard_log:
                          tf.summary.histogram('discounted_rewards', self.rewards_ph)
                          tf.summary.histogram('learning_rate', self.learning_rate)
                          tf.summary.histogram('advantage', self.advs_ph)
                          if tf_util.is_image(self.observation_space):
                              tf.summary.image('observation', train_model.obs_ph)
                          else:
                              tf.summary.histogram('observation', train_model.obs_ph)
  
                  trainer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate_ph, decay=self.alpha,
                                                      epsilon=self.epsilon)
                  self.apply_backprop = trainer.apply_gradients(grads)
  
                  self.train_model = train_model
                  self.step_model = step_model
                  self.step = step_model.step
                  self.proba_step = step_model.proba_step
                  self.value = step_model.value
                  self.initial_state = step_model.initial_state
                  tf.global_variables_initializer().run(session=self.sess)
  
                  self.summary = tf.summary.merge_all()
  
      def _train_step(self, obs, states, rewards, masks, actions, values, update, writer=None):
          """
          applies a training step to the model
  
          :param obs: ([float]) The input observations
          :param states: ([float]) The states (used for recurrent policies)
          :param rewards: ([float]) The rewards from the environment
          :param masks: ([bool]) Whether or not the episode is over (used for recurrent policies)
          :param actions: ([float]) The actions taken
          :param values: ([float]) The logits values
          :param update: (int) the current step iteration
          :param writer: (TensorFlow Summary.writer) the writer for tensorboard
          :return: (float, float, float) policy loss, value loss, policy entropy
          """
          advs = rewards - values
          cur_lr = None
          for _ in range(len(obs)):
              cur_lr = self.learning_rate_schedule.value()
          assert cur_lr is not None, "Error: the observation input array cannon be empty"
  
          td_map = {self.train_model.obs_ph: obs, self.actions_ph: actions, self.advs_ph: advs,
                    self.rewards_ph: rewards, self.learning_rate_ph: cur_lr}
          if states is not None:
              td_map[self.train_model.states_ph] = states
              td_map[self.train_model.dones_ph] = masks
  
          if writer is not None:
              # run loss backprop with summary, but once every 10 runs save the metadata (memory, compute time, ...)
              if self.full_tensorboard_log and (1 + update) % 10 == 0:
                  run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
                  run_metadata = tf.RunMetadata()
                  summary, policy_loss, value_loss, policy_entropy, _ = self.sess.run(
                      [self.summary, self.pg_loss, self.vf_loss, self.entropy, self.apply_backprop],
                      td_map, options=run_options, run_metadata=run_metadata)
                  writer.add_run_metadata(run_metadata, 'step%d' % (update * (self.n_batch + 1)))
              else:
                  summary, policy_loss, value_loss, policy_entropy, _ = self.sess.run(
                      [self.summary, self.pg_loss, self.vf_loss, self.entropy, self.apply_backprop], td_map)
              writer.add_summary(summary, update * (self.n_batch + 1))
  
          else:
              policy_loss, value_loss, policy_entropy, _ = self.sess.run(
                  [self.pg_loss, self.vf_loss, self.entropy, self.apply_backprop], td_map)
  
          return policy_loss, value_loss, policy_entropy
  
      def learn(self, total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name="A2C",
                reset_num_timesteps=True):
  
          new_tb_log = self._init_num_timesteps(reset_num_timesteps)
  
          with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
                  as writer:
              self._setup_learn(seed)
  
              self.learning_rate_schedule = Scheduler(initial_value=self.learning_rate, n_values=total_timesteps,
                                                      schedule=self.lr_schedule)
  
              runner = A2CRunner(self.env, self, n_steps=self.n_steps, gamma=self.gamma)
              self.episode_reward = np.zeros((self.n_envs,))
              # Training stats (when using Monitor wrapper)
              ep_info_buf = deque(maxlen=100)
  
              t_start = time.time()
              for update in range(1, total_timesteps // self.n_batch + 1):
                  # true_reward is the reward without discount
                  obs, states, rewards, masks, actions, values, ep_infos, true_reward = runner.run()
                  ep_info_buf.extend(ep_infos)
                  _, value_loss, policy_entropy = self._train_step(obs, states, rewards, masks, actions, values,
                                                                   self.num_timesteps // (self.n_batch + 1), writer)
                  n_seconds = time.time() - t_start
                  fps = int((update * self.n_batch) / n_seconds)
  
                  if writer is not None:
                      self.episode_reward = total_episode_reward_logger(self.episode_reward,
                                                                        true_reward.reshape((self.n_envs, self.n_steps)),
                                                                        masks.reshape((self.n_envs, self.n_steps)),
                                                                        writer, self.num_timesteps)
  
                  self.num_timesteps += self.n_batch + 1
  
                  if callback is not None:
                      # Only stop training if return value is False, not when it is None. This is for backwards
                      # compatibility with callbacks that have no return statement.
                      if callback(locals(), globals()) is False:
                          break
  
                  if self.verbose >= 1 and (update % log_interval == 0 or update == 1):
                      explained_var = explained_variance(values, rewards)
                      logger.record_tabular("nupdates", update)
                      logger.record_tabular("total_timesteps", self.num_timesteps)
                      logger.record_tabular("fps", fps)
                      logger.record_tabular("policy_entropy", float(policy_entropy))
                      logger.record_tabular("value_loss", float(value_loss))
                      logger.record_tabular("explained_variance", float(explained_var))
                      if len(ep_info_buf) > 0 and len(ep_info_buf[0]) > 0:
                          logger.logkv('ep_reward_mean', safe_mean([ep_info['r'] for ep_info in ep_info_buf]))
                          logger.logkv('ep_len_mean', safe_mean([ep_info['l'] for ep_info in ep_info_buf]))
                      logger.dump_tabular()
  
          return self
  
      def save(self, save_path):
          data = {
              "gamma": self.gamma,
              "n_steps": self.n_steps,
              "vf_coef": self.vf_coef,
              "ent_coef": self.ent_coef,
              "max_grad_norm": self.max_grad_norm,
              "learning_rate": self.learning_rate,
              "alpha": self.alpha,
              "epsilon": self.epsilon,
              "lr_schedule": self.lr_schedule,
              "verbose": self.verbose,
              "policy": self.policy,
              "observation_space": self.observation_space,
              "action_space": self.action_space,
              "n_envs": self.n_envs,
              "_vectorize_action": self._vectorize_action,
              "policy_kwargs": self.policy_kwargs
          }
  
          params = self.sess.run(self.params)
  
          self._save_to_file(save_path, data=data, params=params)
  
  
  class A2CRunner(AbstractEnvRunner):
      def __init__(self, env, model, n_steps=5, gamma=0.99):
          """
          A runner to learn the policy of an environment for an a2c model
  
          :param env: (Gym environment) The environment to learn from
          :param model: (Model) The model to learn
          :param n_steps: (int) The number of steps to run for each environment
          :param gamma: (float) Discount factor
          """
          super(A2CRunner, self).__init__(env=env, model=model, n_steps=n_steps)
          self.gamma = gamma
  
      def run(self):
          """
          Run a learning step of the model
  
          :return: ([float], [float], [float], [bool], [float], [float])
                   observations, states, rewards, masks, actions, values
          """
          mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [], [], [], [], []
          mb_states = self.states
          ep_infos = []
          for _ in range(self.n_steps):
              actions, values, states, _ = self.model.step(self.obs, self.states, self.dones)
              mb_obs.append(np.copy(self.obs))
              mb_actions.append(actions)
              mb_values.append(values)
              mb_dones.append(self.dones)
              clipped_actions = actions
              # Clip the actions to avoid out of bound error
              if isinstance(self.env.action_space, gym.spaces.Box):
                  clipped_actions = np.clip(actions, self.env.action_space.low, self.env.action_space.high)
              obs, rewards, dones, infos = self.env.step(clipped_actions)
              for info in infos:
                  maybe_ep_info = info.get('episode')
                  if maybe_ep_info is not None:
                      ep_infos.append(maybe_ep_info)
  
              self.states = states
              self.dones = dones
              self.obs = obs
              mb_rewards.append(rewards)
          mb_dones.append(self.dones)
          # batch of steps to batch of rollouts
          mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype).swapaxes(1, 0).reshape(self.batch_ob_shape)
          mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(0, 1)
          mb_actions = np.asarray(mb_actions, dtype=self.env.action_space.dtype).swapaxes(0, 1)
          mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(0, 1)
          mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(0, 1)
          mb_masks = mb_dones[:, :-1]
          mb_dones = mb_dones[:, 1:]
          true_rewards = np.copy(mb_rewards)
          last_values = self.model.value(self.obs, self.states, self.dones).tolist()
          # discount/bootstrap off value fn
          for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):
              rewards = rewards.tolist()
              dones = dones.tolist()
              if dones[-1] == 0:
                  rewards = discount_with_dones(rewards + [value], dones + [0], self.gamma)[:-1]
              else:
                  rewards = discount_with_dones(rewards, dones, self.gamma)
              mb_rewards[n] = rewards
  
          # convert from [n_env, n_steps, ...] to [n_steps * n_env, ...]
          mb_rewards = mb_rewards.reshape(-1, *mb_rewards.shape[2:])
          mb_actions = mb_actions.reshape(-1, *mb_actions.shape[2:])
          mb_values = mb_values.reshape(-1, *mb_values.shape[2:])
          mb_masks = mb_masks.reshape(-1, *mb_masks.shape[2:])
          true_rewards = true_rewards.reshape(-1, *true_rewards.shape[2:])
          return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values, ep_infos, true_rewards
  
apis: tensorflow.summary.scalar tensorflow.summary.image numpy.asarray numpy.copy tensorflow.variable_scope tensorflow.squeeze tensorflow.summary.histogram tensorflow.global_variables_initializer tensorflow.clip_by_global_norm tensorflow.Graph numpy.zeros tensorflow.train.RMSPropOptimizer tensorflow.gradients tensorflow.RunOptions tensorflow.placeholder tensorflow.summary.merge_all tensorflow.reduce_mean tensorflow.RunMetadata numpy.clip