docstr: A runner to learn the policy of an environment for a 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
  :param lam: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator
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code: def __init__(self, *, env, model, n_steps, gamma, lam):
      """
      A runner to learn the policy of an environment for a 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
      :param lam: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator
      """
      super().__init__(env=env, model=model, n_steps=n_steps)
      self.lam = lam
      self.gamma = gamma
  
imports: import time
  import gym
  import numpy
  import tensorflow
  from stable_baselines import logger
  from stable_baselines.common import explained_variance, ActorCriticRLModel, tf_util, SetVerbosity, TensorboardWriter
  from stable_baselines.common.runners import AbstractEnvRunner
  from stable_baselines.common.policies import ActorCriticPolicy, RecurrentActorCriticPolicy
  from stable_baselines.common.schedules import get_schedule_fn
  from stable_baselines.common.tf_util import total_episode_reward_logger
  from stable_baselines.common.math_util import safe_mean
label_desc: Use image node_id 1 to create a new Runner object from inherited base classes: AbstractEnvRunner with example: obj = Runner()