class_docstr: A DDPG model based on https://arxiv.org/pdf/1509.02971.pdf.
  
  DDPG, also known as Deep Deterministic Policy Gradient, uses actor-critic
  method to optimize the policy and reward prediction. It uses a supervised
  method to update the critic network and policy gradient to update the actor
  network. And there are exploration strategy, replay buffer and target
  networks involved to stabilize the training process.
  
  Example:
      $ python garage/examples/tf/ddpg_pendulum.py
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (Policy): Policy.
      qf (object): The q value network.
      replay_buffer (ReplayBuffer): Replay buffer.
      sampler (garage.sampler.Sampler): Sampler.
      steps_per_epoch (int): Number of train_once calls per epoch.
      n_train_steps (int): Training steps.
      buffer_batch_size (int): Batch size of replay buffer.
      max_episode_length_eval (int or None): Maximum length of episodes used
          for off-policy evaluation. If None, defaults to
          `env_spec.max_episode_length`.
      min_buffer_size (int): The minimum buffer size for replay buffer.
      exploration_policy (ExplorationPolicy): Exploration strategy.
      target_update_tau (float): Interpolation parameter for doing the
          soft target update.
      policy_lr (float): Learning rate for training policy network.
      qf_lr (float): Learning rate for training q value network.
      discount(float): Discount factor for the cumulative return.
      policy_weight_decay (float): L2 regularization factor for parameters
          of the policy network. Value of 0 means no regularization.
      qf_weight_decay (float): L2 regularization factor for parameters
          of the q value network. Value of 0 means no regularization.
      policy_optimizer (tf.compat.v1.train.Optimizer): Optimizer for training
          policy network.
      qf_optimizer (tf.compat.v1.train.Optimizer): Optimizer for training
          Q-function network.
      clip_pos_returns (bool): Whether or not clip positive returns.
      clip_return (float): Clip return to be in [-clip_return,
          clip_return].
      max_action (float): Maximum action magnitude.
      reward_scale (float): Reward scale.
      name (str): Name of the algorithm shown in computation graph.
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docstr: Obtain samplers and start actual training for each epoch.
  
  Args:
      trainer (Trainer): Experiment trainer, which provides services
          such as snapshotting and sampler control.
  
  Returns:
      float: The average return in last epoch cycle.
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code: def train(self, trainer):
      """Obtain samplers and start actual training for each epoch.
  
      Args:
          trainer (Trainer): Experiment trainer, which provides services
              such as snapshotting and sampler control.
  
      Returns:
          float: The average return in last epoch cycle.
  
      """
      if not self._eval_env:
          self._eval_env = trainer.get_env_copy()
      last_returns = [float("nan")]
      trainer.enable_logging = False
  
      for _ in trainer.step_epochs():
          for cycle in range(self._steps_per_epoch):
              trainer.step_episode = trainer.obtain_episodes(
                  trainer.step_itr
              )
              if hasattr(self.exploration_policy, "update"):
                  self.exploration_policy.update(trainer.step_episode)
              self._train_once(trainer.step_itr, trainer.step_episode)
              if (
                  cycle == 0
                  and self._replay_buffer.n_transitions_stored
                  >= self._min_buffer_size
              ):
                  trainer.enable_logging = True
                  eval_episodes = obtain_evaluation_episodes(
                      self.policy, self._eval_env
                  )
                  last_returns = log_performance(
                      trainer.step_itr,
                      eval_episodes,
                      discount=self._discount,
                  )
              trainer.step_itr += 1
  
      return np.mean(last_returns)
  
file_path: garage/src/garage/tf/algos/ddpg.py
imports: from dowel import logger, tabular
  import numpy
  import tensorflow
  from garage import _Default, log_performance, make_optimizer, obtain_evaluation_episodes
  from garage.np.algos import RLAlgorithm
  from garage.tf import compile_function, get_target_ops
label_desc: Use image node_id 5 for calling the DDPG obj's underlying member method code with example usage: obj.train(trainer) and returns: np