class_docstr: Policy object that implements a DQN policy
  
  :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 scale: (bool) whether or not to scale the input
  :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
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docstr: Returns the action probability for a single step
  
  :param obs: (np.ndarray float or int) The current observation of the environment
  :param state: (np.ndarray float) The last states (used in recurrent policies)
  :param mask: (np.ndarray float) The last masks (used in recurrent policies)
  :return: (np.ndarray float) the action probability
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code: def proba_step(self, obs, state=None, mask=None):
      """
      Returns the action probability for a single step
  
      :param obs: (np.ndarray float or int) The current observation of the environment
      :param state: (np.ndarray float) The last states (used in recurrent policies)
      :param mask: (np.ndarray float) The last masks (used in recurrent policies)
      :return: (np.ndarray float) the action probability
      """
      raise NotImplementedError
  
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 4 for calling the DQNPolicy obj's underlying member method code with example usage: obj.proba_step(obs, state, mask) without return types