class_docstr: Policy object that implements a DDPG-like actor critic, using a feed forward neural network.
  
  :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 layers: ([int]) The size of the Neural network for the policy (if None, default to [64, 64])
  :param cnn_extractor: (function (TensorFlow Tensor, ``**kwargs``): (TensorFlow Tensor)) the CNN feature extraction
  :param feature_extraction: (str) The feature extraction type ("cnn" or "mlp")
  :param layer_norm: (bool) enable layer normalisation
  :param act_fun: (tf.func) the activation function to use in the neural network.
  :param kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
class_docstr_tok: ["Policy","object","that","implements","a","DDPG-like","actor","critic",",","using","a","feed","forward","neural","network",".",":","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","layers",":","(","[","int","]",")","The","size","of","the","Neural","network","for","the","policy","(","if","None",",","default","to","[","64",",","64","]",")",":","param","cnn_extractor",":","(","function","(","TensorFlow","Tensor",",","``","*","*","kwargs","``",")",":","(","TensorFlow","Tensor",")",")","the","CNN","feature","extraction",":","param","feature_extraction",":","(","str",")","The","feature","extraction","type","(","``","cnn","''","or","``","mlp","''",")",":","param","layer_norm",":","(","bool",")","enable","layer","normalisation",":","param","act_fun",":","(","tf.func",")","the","activation","function","to","use","in","the","neural","network",".",":","param","kwargs",":","(","dict",")","Extra","keyword","arguments","for","the","nature","CNN","feature","extraction"]
code: def proba_step(self, obs, state=None, mask=None):
      return self.sess.run(self.policy, {self.obs_ph: obs})
  
imports: import tensorflow
  from gym.spaces import Box
  from stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy
label_desc: Use image node_id 5 for calling the FeedForwardPolicy obj's underlying member method code with example usage: obj.proba_step(obs, state, mask) and returns: self