class_docstr: Simple integration/smoke test for the distributed agent.
code: def test_agent(self, distributional_critic):
      # Create objectives.
      reward_objectives, qvalue_objectives = make_objectives()
  
      network_factory = lp_utils.partial_kwargs(
          make_networks, distributional_critic=distributional_critic
      )
  
      agent = mompo.DistributedMultiObjectiveMPO(
          reward_objectives,
          qvalue_objectives,
          environment_factory=make_environment,
          network_factory=network_factory,
          num_actors=2,
          batch_size=32,
          min_replay_size=32,
          max_replay_size=1000,
      )
      program = agent.build()
  
      (learner_node,) = program.groups["learner"]
      learner_node.disable_run()
  
      lp.launch(program, launch_type="test_mt")
  
      learner: acme.Learner = learner_node.create_handle().dereference()
  
      for _ in range(5):
          learner.step()
  
imports: from typing import Sequence, Tuple
  import acme
  from acme import specs
  from acme import wrappers
  from acme.agents.tf import mompo
  from acme.tf import networks
  from acme.tf import utils
  from acme.utils import lp_utils
  from dm_control import suite
  import launchpad
  import numpy
  import sonnet
  import tensorflow
  from absl.testing import absltest
  from absl.testing import parameterized
label_desc: Use image node_id 1 for calling the DistributedAgentTest obj's underlying member method code with example usage: obj.test_agent(distributional_critic) without return types