 Here is a basic example of setting up and running a Continuous Q-Learning (CQL) agent using the Acme library. Please note that this is a simplified example and you may need to adjust the parameters according to your specific needs.

```python
import gym
import numpy as np
from acme import learn, environments, agents, runners

# Set up the environment
env_name = "your_environment_name"
env = environments.wrap(gym.make(env_name), max_episode_steps=1000)

# Set up the demonstrations dataset
demonstrations_dataset_name = "your_demonstrations_dataset_name"

# Set up the agent
batch_size = 128
evaluation_period = 1000
num_demonstration_episodes = 10000
random_seed = 42
learning_rates = dict(actor=0.001, critic=0.01)
cql_params = dict(tau=0.005, gamma=0.99, alpha=0.2)

agent = agents.CQL(
    env=env,
    batch_size=batch_size,
    evaluation_period=evaluation_period,
    num_demonstration_episodes=num_demonstration_episodes,
    random_seed=random_seed,
    learning_rates=learning_rates,
    cql_params=cql_params,
)

# Set up the evaluator network
evaluator = agents.Evaluator(agent)

# Set up the environment loop
runner = runners.Sequential(
    [
        runners.Episode(agent),
        runners.Evaluation(evaluator, evaluation_period),
    ]
)

# Run the agent in the environment loop
runner.run(total_episodes=100000)
```

This code creates an environment, sets up a CQL agent with the specified parameters, creates an evaluator, and runs the agent in an environment loop for 100,000 episodes. The demonstrations dataset is loaded automatically if it exists, and the agent learns from both the environment and the demonstrations. The evaluator is used to periodically evaluate the agent's performance.