 Here is a Python code snippet that sets up and runs a Continuous Q-Learning (CQL) agent using the Acme library. Please note that you need to install the Acme library and its dependencies before running this code.

```python
import torch
import torch.nn as nn
import torch.optim as optim
from acme import environments, learners, policies, algorithms, distributions
from acme.buffers.replay_memory import ReplayBuffer
from acme.wrappers import TimeLimit

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

# Set up demonstrations dataset
demonstrations_dataset_name = "your_demonstrations_dataset_name"
demonstrations_dataset = ...  # Load the dataset

# Set up CQL agent
batch_size = 64
evaluation_period = 1000
num_demonstration_episodes = 1000
random_seed = 42
learning_rates = dict(actor=1e-4, critic=1e-3)
cql_params = dict(tau=0.005, gamma=0.99, alpha=0.2)

policy = policies.GaussianLstmPolicy
optimizer = optim.Adam

agent = learners.CqlAgent(
    policy=policy,
    optimizer=optimizer,
    replay_buffer_class=ReplayBuffer,
    evaluation_env=TimeLimit(env, evaluation_period),
    batch_size=batch_size,
    num_eval_episodes=evaluation_period,
    num_demonstration_episodes=num_demonstration_episodes,
    random_seed=random_seed,
    learning_rates=learning_rates,
    cql_params=cql_params,
    demonstrations_dataset=demonstrations_dataset,
)

# Set up evaluator network
evaluator_network = ...  # Define your evaluator network
agent.set_evaluator_network(evaluator_network)

# Run the agent
for i in range(10000):
    state, action, reward, next_state, done = agent.act(env)
    agent.observe(state, action, reward, next_state, done)

    if (i + 1) % evaluation_period == 0:
        scores = agent.evaluate()
        print(f"Episode {i + 1}: Average score: {scores:.2f}")
```

This code sets up a CQL agent with the specified parameters, loads a demonstrations dataset, and runs the agent in an environment loop for 10,000 steps. The agent's networks are created and optimized, and an evaluator network is defined. The agent evaluates itself periodically every `evaluation_period` steps. You need to define your own evaluator network according to your specific requirements.