class_docstr: SQIL Builder (https://openreview.net/pdf?id=S1xKd24twB).
class_docstr_tok: ["SQIL","Builder","(","https",":","\/\/openreview.net\/pdf","?","id=S1xKd24twB",")","."]
docstr: Builds a SQIL agent.
  
  Args:
    rl_agent: An off policy direct RL agent..
    rl_agent_batch_size: The batch size of the above algorithm.
    make_demonstrations: A function that returns an infinite iterator with
      demonstrations.
docstr_tok: ["Builds","a","SQIL","agent",".","Args",":","rl_agent",":","An","off","policy","direct","RL","agent","..","rl_agent_batch_size",":","The","batch","size","of","the","above","algorithm",".","make_demonstrations",":","A","function","that","returns","an","infinite","iterator","with","demonstrations","."]
code: def __init__(
      self,
      rl_agent: builders.ActorLearnerBuilder[
          DirectRLNetworks, DirectPolicyNetwork, reverb.ReplaySample
      ],
      rl_agent_batch_size: int,
      make_demonstrations: Callable[[int], Iterator[types.Transition]],
  ):
      """Builds a SQIL agent.
  
      Args:
        rl_agent: An off policy direct RL agent..
        rl_agent_batch_size: The batch size of the above algorithm.
        make_demonstrations: A function that returns an infinite iterator with
          demonstrations.
      """
      self._rl_agent = rl_agent
      self._rl_agent_batch_size = rl_agent_batch_size
      self._make_demonstrations = make_demonstrations
  
imports: from typing import Callable, Generic, Iterator, List, Optional
  from acme import adders
  from acme import core
  from acme import specs
  from acme import types
  from acme.agents.jax import builders
  from acme.jax import networks
  from acme.jax import utils
  from acme.jax.imitation_learning_types import DirectPolicyNetwork, DirectRLNetworks
  from acme.utils import counting
  from acme.utils import loggers
  import jax
  import numpy
  import reverb
  import tree
label_desc: Use image node_id 1 to create a new SQILBuilder object from inherited base classes: Generic, builders.ActorLearnerBuilder with example: obj = SQILBuilder(rl_agent, rl_agent_batch_size, make_demonstrations)