class_docstr: 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 calculation
  :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 momentum: (float) RMSProp momentum parameter (default: 0.0)
  :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
  :param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
      If None (default), use random seed. Note that if you want completely deterministic
      results, you must set `n_cpu_tf_sess` to 1.
  :param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
      If None, the number of cpu of the current machine will be used.
class_docstr_tok: ["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","calculation",":","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","momentum",":","(","float",")","RMSProp","momentum","parameter","(","default",":","0.0",")",":","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",":","param","seed",":","(","int",")","Seed","for","the","pseudo-random","generators","(","python",",","numpy",",","tensorflow",")",".","If","None","(","default",")",",","use","random","seed",".","Note","that","if","you","want","completely","deterministic","results",",","you","must","set","`","n_cpu_tf_sess","`","to","1.",":","param","n_cpu_tf_sess",":","(","int",")","The","number","of","threads","for","TensorFlow","operations","If","None",",","the","number","of","cpu","of","the","current","machine","will","be","used","."]
code: 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.set_random_seed(self.seed)
              self.sess = tf_util.make_session(
                  num_cpu=self.n_cpu_tf_sess, 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 = tf_util.get_trainable_vars("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_ph),
                  )
                  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_ph
                      )
                      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,
                  momentum=self.momentum,
              )
              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()
  
imports: import time
  import gym
  import numpy
  import tensorflow
  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.common.schedules import Scheduler
  from stable_baselines.common.tf_util import mse, total_episode_reward_logger
  from stable_baselines.common.math_util import safe_mean
label_desc: Use image node_id 4 for calling the A2C obj's underlying member method code with example usage: obj.setup_model() without return types