 Here is a basic structure of the Python code based on your instructions. Please note that this is a simplified version and you may need to adjust it according to your specific needs and the actual Acme library APIs.

```python
import tensorflow as tf
from acme import environment_factory, network_factory, RunExperimentTest

# Flags for configuring the experiment
flags = tf.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('environment', 'my_env', 'Name of the environment')
flags.DEFINE_integer('num_demonstrations', 1000, 'Number of demonstrations')
flags.DEFINE_integer('learning_steps', 10000, 'Number of learning steps')
flags.DEFINE_integer('batch_size', 64, 'Batch size')
flags.DEFINE_float('learning_rate', 0.001, 'Learning rate')
flags.DEFINE_float('dropout_rate', 0.5, 'Dropout rate')
flags.DEFINE_integer('hidden_units', 64, 'Number of hidden units')

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

def create_environment_factory():
    # Implement your environment factory here
    pass

def create_network_factory():
    def _create_network():
        inputs = tf.placeholder(tf.float32, [None, ...])
        net = tf.layers.dense(inputs, FLAGS.hidden_units, activation=tf.nn.relu)
        net = tf.layers.dropout(net, rate=FLAGS.dropout_rate)
        # Add output layer and loss function here
        return net

    return network_factory.NetworkFactory(_create_network)

def build_experiment_config():
    return ExperimentConfig(
        environment_factory=create_environment_factory(),
        demonstration_dataset_factory=create_demonstration_dataset_factory(),
        network_factory=create_network_factory(),
        learning_steps=FLAGS.learning_steps,
        batch_size=FLAGS.batch_size,
        learning_rate=FLAGS.learning_rate,
        run_mode=RunExperimentTest.Mode.ONLINE if FLAGS.distributed else RunExperimentTest.Mode.SINGLE_THREAD,
    )

def main(_):
    config = build_experiment_config()
    if FLAGS.distributed:
        config = make_distributed_experiment(config)

    run_experiment(config)

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

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 in either a distributed or single-threaded manner. You will need to implement the specific details for the demonstration dataset factory, environment factory, and the network creation within the network factory function.