 Here is a simplified example of how you might create a CRR agent using the Acme library in Python. Please note that this is a basic example and might need adjustments based on your specific requirements. Also, Acme library is a fictional library, so you might need to replace it with a real library like Stable Baselines3 or Deepmind Lab.

```python
import gym
import numpy as np
import acme
import acme.agents.categorical.crr as crr
import acme.buffers as buffers
import acme.learners.sac as sac
import acme.policies.categorical as policies
import acme.specs as specs

def add_next_action_extras(observation, action, reward, next_observation, done):
    return observation, action, reward, next_observation, done, action

def create_agent(env_spec, dataset_name, batch_size, eval_period, demo_episodes, seed, lr, discount, tau, use_sarsa_target):
    env = gym.make(env_spec)
    dataset = load_dataset(dataset_name)
    policy = policies.CategoricalMLPPolicy(env_spec=env_spec, hidden_sizes=(64, 64))
    learner = sac.SAC(
        policy=policy,
        env_spec=env_spec,
        batch_size=batch_size,
        learning_rate=lr,
        discount=discount,
        tau=tau,
        use_sarsa_target=use_sarsa_target,
    )
    evaluator = policies.CategoricalMLPPolicy(env_spec=env_spec, hidden_sizes=(64, 64))
    buffer = buffers.ReplayBuffer(
        capacity=int(1e6),
        observation_space=env_spec.observation_space,
        action_space=env_spec.action_space,
        transition_modifier=add_next_action_extras,
    )
    agent = crr.CRRAgent(
        policy=policy,
        learner=learner,
        evaluator=evaluator,
        buffer=buffer,
        eval_period=eval_period,
        demo_episodes=demo_episodes,
        seed=seed,
    )
    return agent

def main():
    env_spec = 'MyEnvironment'
    dataset_name = 'MyDataset'
    batch_size = 128
    eval_period = 1000
    demo_episodes = 1000
    seed = 42
    lr = 3e-4
    discount = 0.99
    tau = 0.005
    use_sarsa_target = False

    agent = create_agent(env_spec, dataset_name, batch_size, eval_period, demo_episodes, seed, lr, discount, tau, use_sarsa_target)

    # ... (environment loop code here)

if __name__ == "__main__":
    main()
```

This code creates an agent with the specified parameters, but it does not include the environment loop. You would need to implement the environment loop according to the Acme library's documentation or the specific library you are using. Also, the `load_dataset` function is a placeholder for a function that loads your dataset. You would need to implement this function according to your specific dataset requirements.