class_docstr: Policy object that implements a DDPG-like actor critic
  
  :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
class_docstr_tok: ["Policy","object","that","implements","a","DDPG-like","actor","critic",":","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"]
docstr: Returns the action probability for a single step
  
  :param obs: ([float] or [int]) The current observation of the environment
  :param state: ([float]) The last states (used in recurrent policies)
  :param mask: ([float]) The last masks (used in recurrent policies)
  :return: ([float]) the action probability
docstr_tok: ["Returns","the","action","probability","for","a","single","step",":","param","obs",":","(","[","float","]","or","[","int","]",")","The","current","observation","of","the","environment",":","param","state",":","(","[","float","]",")","The","last","states","(","used","in","recurrent","policies",")",":","param","mask",":","(","[","float","]",")","The","last","masks","(","used","in","recurrent","policies",")",":","return",":","(","[","float","]",")","the","action","probability"]
code: def proba_step(self, obs, state=None, mask=None):
      """
      Returns the action probability for a single step
  
      :param obs: ([float] or [int]) The current observation of the environment
      :param state: ([float]) The last states (used in recurrent policies)
      :param mask: ([float]) The last masks (used in recurrent policies)
      :return: ([float]) the action probability
      """
      raise NotImplementedError
  
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 DDPGPolicy obj's underlying member method code with example usage: obj.proba_step(obs, state, mask) without return types