 Here is a simplified example of how you might set up a behavioral cloning experiment using the Acme library in Python. Please note that this is a basic example and does not include all the necessary imports or error handling.

```python
import torch
import torch.nn as nn
import torch.optim as optim
from acme import environments, learners, runners, policies

# Flags for configuring the experiment
FLAGS = flags.FLAGS
FLAGS.define_flag('environment', default='MyEnvironment', help='Name of the environment')
FLAGS.define_flag('num_demonstrations', default=1000, help='Number of demonstrations')
FLAGS.define_flag('learning_steps', default=1000, help='Number of learning steps')
FLAGS.define_flag('batch_size', default=64, help='Batch size')
FLAGS.define_flag('learning_rate', default=0.001, help='Learning rate')
FLAGS.define_flag('dropout_rate', default=0.2, help='Dropout rate')
FLAGS.define_flag('hidden_units', default=[64, 64], help='Number of hidden units')

def create_demonstration_dataset_factory(env):
    # Implement your demonstration dataset factory here
    pass

def create_environment_factory(env_name):
    # Implement your environment factory here
    return environments.wrap(environments.make(env_name), {'max_episode_steps': 1000})

def create_network_factory():
    def network_factory():
        net = nn.Sequential(
            nn.Linear(env.observation_space.shape[0], hidden_units[0]),
            nn.ReLU(),
            nn.Dropout(dropout_rate),
            *[nn.Linear(hidden_units[i], hidden_units[i+1]) for i in range(len(hidden_units)-1)],
            nn.ReLU(),
            nn.Dropout(dropout_rate),
            nn.Linear(hidden_units[-1], env.action_space.n)
        )
        return net
    return network_factory

def build_experiment_config():
    return learners.BehavioralCloning(
        policy=policies.EpsGreedy(network_factory()),
        dataset=create_demonstration_dataset_factory(env),
        optimizer=optim.Adam(lr=learning_rate),
        loss_fn=nn.CrossEntropyLoss(),
        batch_size=batch_size,
        learning_steps_per_update=learning_steps // num_demonstrations,
    )

def main():
    env = create_environment_factory(FLAGS.environment)
    config = build_experiment_config()
    runner = runners.SequentialRunner(env, config)
    if FLAGS.distributed:
        runner = runners.DistributedRunner(runner)
    runner.run(total_timesteps=FLAGS.learning_steps * num_demonstrations)

if __name__ == '__main__':
    tf.app.run()
```

This code defines flags for configuring the experiment, creates factories for the demonstration dataset, environment, and network, builds the experiment configuration, and runs the experiment. The network is a multi-layer perceptron with ReLU activation and dropout. The main function runs the experiment either in a distributed or single-threaded manner.