docstr: 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
docstr_tok: ["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"]
code: 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
  
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 1 to create a new A2CRunner object from inherited base classes: AbstractEnvRunner with example: obj = A2CRunner(env, model, n_steps, gamma)