  Here is an example of Python code that sets up and runs a Continuous Q-Learning (CQL) agent on a specified environment using the Acme library:
```
import acme

# Set up the environment
env = acme.Environment(name='CartPole-v1')

# Set up the agent
agent = acme.Agent(
    environment=env,
    batch_size=32,
    evaluation_period=100,
    number_of_demonstration_episodes=1000,
    random_seed=42,
    learning_rate=0.001,
    cql_specific_parameters={
        'alpha': 0.1,
        'beta': 0.5,
        'gamma': 0.99,
        'epsilon': 0.1,
        'tau': 0.01
    }
)

# Set up the networks
agent.networks = {
    'q_network': acme.Network(
        environment=env,
        batch_size=32,
        learning_rate=0.001,
        cql_specific_parameters={
            'alpha': 0.1,
            'beta': 0.5,
            'gamma': 0.99,
            'epsilon': 0.1,
            'tau': 0.01
        }
    ),
    'target_q_network': acme.Network(
        environment=env,
        batch_size=32,
        learning_rate=0.001,
        cql_specific_parameters={
            'alpha': 0.1,
            'beta': 0.5,
            'gamma': 0.99,
            'epsilon': 0.1,
            'tau': 0.01
        }
    )
}

# Set up the evaluator network
agent.evaluator_network = acme.Network(
    environment=env,
    batch_size=32,
    learning_rate=0.001,
    cql_specific_parameters={
        'alpha': 0.1,
        'beta': 0.5,
        'gamma': 0.99,
        'epsilon': 0.1,
        'tau': 0.01
    }
)

# Run the agent in an environment loop
while not agent.should_terminate():
    agent.update_and_wait()
    agent.evaluate()
```
This code sets up a CartPole-v1 environment and an agent with various parameters such as batch size, evaluation period, number of demonstration episodes, random seed, learning rates, and CQL specific parameters. The agent's networks are also set up and optimized, and an evaluator network is defined. The agent is then run in an environment loop, where it learns and evaluates itself periodically.