```python
import acme
from acme import specs
from acme.agents.tf import cql
from acme.tf import networks
from acme.utils import loggers
import sonnet as snt
import tensorflow as tf
import dm_env
import numpy as np
import reverb

# Environment setup
environment_name = 'HalfCheetah-v2'  # Example environment name
dataset_name = 'halfcheetah_demos'  # Example dataset name

# Create the environment
environment = dm_env.specs.make_environment_spec(acme.make_environment(environment_name))
environment_spec = specs.make_environment_spec(environment)

# Agent configuration parameters
batch_size = 256
evaluation_period = 10
num_demonstration_episodes = 20
random_seed = 42
learning_rate = 1e-3
cql_alpha = 1.0  # CQL specific parameter

# Set the random seed for reproducibility
tf.random.set_seed(random_seed)
np.random.seed(random_seed)

# Create networks
observation_spec, action_spec, _ = environment_spec
policy_network = snt.Sequential([
    networks.LayerNormMLP(layer_sizes=[256, 256, action_spec.shape[0]]),
    networks.TanhToSpec(spec=action_spec)
])
critic_network = networks.CriticMultiplexer(
    critic_network=networks.LayerNormMLP(layer_sizes=[256, 256, 1]),
    action_network=networks.ClipToSpec(action_spec)
)

# Create a replay buffer
replay_buffer = reverb.Table(
    name='replay_buffer',
    max_size=1000000,
    sampler=reverb.selectors.Uniform(),
    remover=reverb.selectors.Fifo(),
    rate_limiter=reverb.rate_limiters.MinSize(1),
    signature=environment_spec.make_replay_buffer_signature()
)

# Create the CQL agent
agent = cql.CQL(
    environment_spec=environment_spec,
    policy_network=policy_network,
    critic_network=critic_network,
    observation_network=tf.identity,  # Use the identity function for the observation network
    dataset_iterator=dataset_iterator,  # Assuming dataset_iterator is defined to iterate over the specified dataset
    replay_buffer=replay_buffer,
    batch_size=batch_size,
    learning_rate=learning_rate,
    cql_alpha=cql_alpha
)

# Create an environment loop
loop = acme.EnvironmentLoop(environment, agent, logger=loggers.TerminalLogger())

# Run the loop to train the agent
for episode in range(num_demonstration_episodes):
    print(f'Starting episode {episode}')
    loop.run(num_episodes=1)
    if episode % evaluation_period == 0:
        # Evaluate the agent
        print(f'Evaluating at episode {episode}')
        # Evaluation logic here
```
Note: This code assumes that you have access to or have created a `dataset_iterator` that iterates over the specified dataset. You might need to adjust the dataset loading and preprocessing logic based on your specific dataset and requirements.