class_docstr: R2D2 Builder.
  
  This is constructs all of the components for Recurrent Experience Replay in
  Distributed Reinforcement Learning (Kapturowski et al.)
  https://openreview.net/pdf?id=r1lyTjAqYX.
class_docstr_tok: ["R2D2","Builder",".","This","is","constructs","all","of","the","components","for","Recurrent","Experience","Replay","in","Distributed","Reinforcement","Learning","(","Kapturowski","et","al",".",")","https",":","\/\/openreview.net\/pdf","?","id=r1lyTjAqYX","."]
docstr: Creates a R2D2 learner, a behavior policy and an eval actor.
docstr_tok: ["Creates","a","R2D2","learner",",","a","behavior","policy","and","an","eval","actor","."]
code: def __init__(self, config: r2d2_config.R2D2Config):
      """Creates a R2D2 learner, a behavior policy and an eval actor."""
      self._config = config
      self._sequence_length = (
          self._config.burn_in_length + self._config.trace_length + 1
      )
  
imports: from typing import Generic, Iterator, List, Optional
  import acme
  from acme import adders
  from acme import core
  from acme import specs
  from acme.adders import reverb
  from acme.adders.reverb import base
  from acme.adders.reverb import structured
  from acme.agents.jax import actor_core
  from acme.agents.jax import actors
  from acme.agents.jax import builders
  from acme.agents.jax.r2d2 import actor
  from acme.agents.jax.r2d2 import config
  from acme.agents.jax.r2d2 import learning
  from acme.agents.jax.r2d2 import networks
  from acme.datasets import reverb
  from acme.jax import networks
  from acme.jax import utils
  from acme.jax import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  import jax
  import optax
  import reverb
  from reverb import structured_writer
  import tensorflow
  import tree
label_desc: Use image node_id 1 to create a new R2D2Builder object from inherited base classes: Generic, builders.ActorLearnerBuilder with example: obj = R2D2Builder(config)