code: def main(_):
      # Create an environment and grab the spec.
      environment = bc_utils.make_environment()
      environment_spec = specs.make_environment_spec(environment)
  
      # Unwrap the environment to get the demonstrations.
      dataset = bc_utils.make_demonstrations(
          environment.environment, FLAGS.batch_size
      )
      dataset = dataset.as_numpy_iterator()
  
      # Create the networks to optimize.
      bc_networks = bc_utils.make_network(environment_spec)
  
      key = jax.random.PRNGKey(FLAGS.seed)
      key, key1 = jax.random.split(key, 2)
  
      loss_fn = bc.logp()
  
      learner = bc.BCLearner(
          networks=bc_networks,
          random_key=key1,
          loss_fn=loss_fn,
          optimizer=optax.adam(FLAGS.learning_rate),
          prefetching_iterator=utils.sharded_prefetch(dataset),
          num_sgd_steps_per_step=1,
      )
  
      def evaluator_network(
          params: hk.Params, key: jax.Array, observation: jax.Array
      ) -> jax.Array:
          dist_params = bc_networks.policy_network.apply(
              params, observation
          )
          return rlax.epsilon_greedy(FLAGS.evaluation_epsilon).sample(
              key, dist_params
          )
  
      actor_core = actor_core_lib.batched_feed_forward_to_actor_core(
          evaluator_network
      )
      variable_client = variable_utils.VariableClient(
          learner, "policy", device="cpu"
      )
      evaluator = actors.GenericActor(
          actor_core, key, variable_client, backend="cpu"
      )
  
      eval_loop = acme.EnvironmentLoop(
          environment=environment,
          actor=evaluator,
          logger=loggers.TerminalLogger("evaluation", time_delta=0.0),
      )
  
      # Run the environment loop.
      while True:
          for _ in range(FLAGS.evaluate_every):
              learner.step()
          eval_loop.run(FLAGS.evaluation_episodes)
  
file_path: acme/examples/offline/run_bc_jax.py
imports: from absl import app
  from absl import flags
  import acme
  from acme import specs
  from acme.agents.jax import actor_core
  from acme.agents.jax import actors
  from acme.agents.jax import bc
  from acme.examples.offline import bc_utils
  from acme.jax import utils
  from acme.jax import variable_utils
  from acme.utils import loggers
  import haiku
  import jax
  import optax
  import rlax
label_desc: Use image node_id 1 for calling a global function with example usage: main(_) and returns: rlax