code: def build_experiment_config():
      """Builds D4PG experiment config which can be executed in different ways."""
  
      # Create an environment, grab the spec, and use it to create networks.
      suite, task = FLAGS.env_name.split(":", 1)
  
      # Bound of the distributional critic. The reward for control environments is
      # normalized, not for gym locomotion environments hence the different scales.
      vmax_values = {
          "gym": 1000.0,
          "control": 150.0,
      }
      vmax = vmax_values[suite]
  
      def network_factory(spec) -> d4pg.D4PGNetworks:
          return d4pg.make_networks(
              spec,
              policy_layer_sizes=(256, 256, 256),
              critic_layer_sizes=(256, 256, 256),
              vmin=-vmax,
              vmax=vmax,
          )
  
      # Configure the agent.
      d4pg_config = d4pg.D4PGConfig(learning_rate=3e-4, sigma=0.2)
  
      return experiments.ExperimentConfig(
          builder=d4pg.D4PGBuilder(d4pg_config),
          environment_factory=lambda seed: helpers.make_environment(
              suite, task
          ),
          network_factory=network_factory,
          seed=FLAGS.seed,
          max_num_actor_steps=FLAGS.num_steps,
      )
  
file_path: acme/examples/baselines/rl_continuous/run_d4pg.py
imports: from absl import flags
  from acme.agents.jax import d4pg
  import helpers
  from absl import app
  from acme.jax import experiments
  from acme.utils import lp_utils
  import launchpad
label_desc: Use image node_id 1 for calling a global function with example usage: build_experiment_config() and returns: experiments, d4pg