class_docstr: Policy object that implements DQN policy, using a MLP (2 layers of 64)
  
  :param sess: (TensorFlow session) The current TensorFlow session
  :param ob_space: (Gym Space) The observation space of the environment
  :param ac_space: (Gym Space) The action space of the environment
  :param n_env: (int) The number of environments to run
  :param n_steps: (int) The number of steps to run for each environment
  :param n_batch: (int) The number of batch to run (n_envs * n_steps)
  :param reuse: (bool) If the policy is reusable or not
  :param obs_phs: (TensorFlow Tensor, TensorFlow Tensor) a tuple containing an override for observation placeholder
      and the processed observation placeholder respectively
  :param dueling: (bool) if true double the output MLP to compute a baseline for action scores
  :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
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code: def __init__(
      self,
      sess,
      ob_space,
      ac_space,
      n_env,
      n_steps,
      n_batch,
      reuse=False,
      obs_phs=None,
      dueling=True,
      **_kwargs,
  ):
      super(MlpPolicy, self).__init__(
          sess,
          ob_space,
          ac_space,
          n_env,
          n_steps,
          n_batch,
          reuse,
          feature_extraction="mlp",
          obs_phs=obs_phs,
          dueling=dueling,
          layer_norm=False,
          **_kwargs,
      )
  
imports: import tensorflow
  import tensorflow.contrib.layers
  import numpy
  from gym.spaces import Discrete
  from stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy
label_desc: Use image node_id 1 to create a new MlpPolicy object from inherited base classes: FeedForwardPolicy with example: obj = MlpPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, obs_phs, dueling)