class_docstr: Deep Deterministic Policy Gradient (DDPG) model
  
  DDPG: https://arxiv.org/pdf/1509.02971.pdf
  
  :param policy: (DDPGPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...)
  :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
  :param gamma: (float) the discount factor
  :param memory_policy: (ReplayBuffer) the replay buffer
      (if None, default to baselines.deepq.replay_buffer.ReplayBuffer)
  
      .. deprecated:: 2.6.0
          This parameter will be removed in a future version
  
  :param eval_env: (Gym Environment) the evaluation environment (can be None)
  :param nb_train_steps: (int) the number of training steps
  :param nb_rollout_steps: (int) the number of rollout steps
  :param nb_eval_steps: (int) the number of evaluation steps
  :param param_noise: (AdaptiveParamNoiseSpec) the parameter noise type (can be None)
  :param action_noise: (ActionNoise) the action noise type (can be None)
  :param param_noise_adaption_interval: (int) apply param noise every N steps
  :param tau: (float) the soft update coefficient (keep old values, between 0 and 1)
  :param normalize_returns: (bool) should the critic output be normalized
  :param enable_popart: (bool) enable pop-art normalization of the critic output
      (https://arxiv.org/pdf/1602.07714.pdf), normalize_returns must be set to True.
  :param normalize_observations: (bool) should the observation be normalized
  :param batch_size: (int) the size of the batch for learning the policy
  :param observation_range: (tuple) the bounding values for the observation
  :param return_range: (tuple) the bounding values for the critic output
  :param critic_l2_reg: (float) l2 regularizer coefficient
  :param actor_lr: (float) the actor learning rate
  :param critic_lr: (float) the critic learning rate
  :param clip_norm: (float) clip the gradients (disabled if None)
  :param reward_scale: (float) the value the reward should be scaled by
  :param render: (bool) enable rendering of the environment
  :param render_eval: (bool) enable rendering of the evaluation environment
  :param memory_limit: (int) the max number of transitions to store, size of the replay buffer
  
      .. deprecated:: 2.6.0
          Use `buffer_size` instead.
  
  :param buffer_size: (int) the max number of transitions to store, size of the replay buffer
  :param random_exploration: (float) Probability of taking a random action (as in an epsilon-greedy strategy)
      This is not needed for DDPG normally but can help exploring when using HER + DDPG.
      This hack was present in the original OpenAI Baselines repo (DDPG + HER)
  :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
  :param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
  :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
  :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
  :param full_tensorboard_log: (bool) enable additional logging when using tensorboard
      WARNING: this logging can take a lot of space quickly
  :param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
      If None (default), use random seed. Note that if you want completely deterministic
      results, you must set `n_cpu_tf_sess` to 1.
  :param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
      If None, the number of cpu of the current machine will be used.
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docstr: Setup the parameter noise operations
  
  :param normalized_obs: (TensorFlow Tensor) the normalized observation
docstr_tok: ["Setup","the","parameter","noise","operations",":","param","normalized_obs",":","(","TensorFlow","Tensor",")","the","normalized","observation"]
code: def _setup_param_noise(self, normalized_obs):
      """
      Setup the parameter noise operations
  
      :param normalized_obs: (TensorFlow Tensor) the normalized observation
      """
      assert self.param_noise is not None
  
      with tf.variable_scope("noise", reuse=False):
          self.perturbed_actor_tf = self.param_noise_actor.make_actor(
              normalized_obs
          )
  
      with tf.variable_scope("noise_adapt", reuse=False):
          adaptive_actor_tf = (
              self.adaptive_param_noise_actor.make_actor(normalized_obs)
          )
  
      with tf.variable_scope("noise_update_func", reuse=False):
          if self.verbose >= 2:
              logger.info("setting up param noise")
          self.perturb_policy_ops = get_perturbed_actor_updates(
              "model/pi/",
              "noise/pi/",
              self.param_noise_stddev,
              verbose=self.verbose,
          )
  
          self.perturb_adaptive_policy_ops = (
              get_perturbed_actor_updates(
                  "model/pi/",
                  "noise_adapt/pi/",
                  self.param_noise_stddev,
                  verbose=self.verbose,
              )
          )
          self.adaptive_policy_distance = tf.sqrt(
              tf.reduce_mean(
                  tf.square(self.actor_tf - adaptive_actor_tf)
              )
          )
  
imports: from functools import reduce
  import os
  import time
  from collections import deque
  import pickle
  import warnings
  import gym
  import numpy
  import tensorflow
  import tensorflow.contrib
  from mpi4py import MPI
  from stable_baselines import logger
  from stable_baselines.common import tf_util, OffPolicyRLModel, SetVerbosity, TensorboardWriter
  from stable_baselines.common.vec_env import VecEnv
  from stable_baselines.common.mpi_adam import MpiAdam
  from stable_baselines.common.buffers import ReplayBuffer
  from stable_baselines.common.math_util import unscale_action, scale_action
  from stable_baselines.common.mpi_running_mean_std import RunningMeanStd
  from stable_baselines.ddpg.policies import DDPGPolicy
label_desc: Use image node_id 5 for calling the DDPG obj's underlying member method code with example usage: obj._setup_param_noise(normalized_obs) without return types