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
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code: def __init__(
      self,
      sess,
      ob_space,
      ac_space,
      n_env,
      n_steps,
      n_batch,
      reuse=False,
      scale=False,
  ):
      super(DDPGPolicy, self).__init__(
          sess,
          ob_space,
          ac_space,
          n_env,
          n_steps,
          n_batch,
          reuse=reuse,
          scale=scale,
          add_action_ph=True,
      )
      assert isinstance(
          ac_space, Box
      ), "Error: the action space must be of type gym.spaces.Box"
      self.qvalue_fn = None
      self.policy = None
  
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 1 to create a new DDPGPolicy object from inherited base classes: BasePolicy with example: obj = DDPGPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, scale)