class_docstr: Program definition for distributed DDPG (D3PG).
class_docstr_tok: ["Program","definition","for","distributed","DDPG","(","D3PG",")","."]
docstr: The replay storage.
docstr_tok: ["The","replay","storage","."]
code: def replay(self):
      """The replay storage."""
      if self._samples_per_insert is not None:
          # Create enough of an error buffer to give a 10% tolerance in rate.
          samples_per_insert_tolerance = 0.1 * self._samples_per_insert
          error_buffer = (
              self._min_replay_size * samples_per_insert_tolerance
          )
  
          limiter = reverb.rate_limiters.SampleToInsertRatio(
              min_size_to_sample=self._min_replay_size,
              samples_per_insert=self._samples_per_insert,
              error_buffer=error_buffer,
          )
      else:
          limiter = reverb.rate_limiters.MinSize(self._min_replay_size)
      replay_table = reverb.Table(
          name=adders.DEFAULT_PRIORITY_TABLE,
          sampler=reverb.selectors.Uniform(),
          remover=reverb.selectors.Fifo(),
          max_size=self._max_replay_size,
          rate_limiter=limiter,
          signature=adders.NStepTransitionAdder.signature(
              self._environment_spec
          ),
      )
      return [replay_table]
  
imports: from typing import Callable, Dict, Optional
  import acme
  from acme import datasets
  from acme import specs
  from acme.adders import reverb
  from acme.agents.tf import actors
  from acme.agents.tf.ddpg import learning
  from acme.tf import networks
  from acme.tf import savers
  from acme.tf import utils
  from acme.tf import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  from acme.utils import lp_utils
  import dm_env
  import launchpad
  import reverb
  import sonnet
  import tensorflow
label_desc: Use image node_id 2 for calling the DistributedDDPG obj's underlying member method code with example usage: obj.replay() and returns: list