  import acme

def create_crr_agent(env, dataset, batch_size, evaluation_period, num_demonstration_episodes, random_seed, learning_rate, discount, target_update_period, use_sarsa_target):
    # Create the environment
    env = acme.Environment(env)

    # Get the demonstrations dataset
    dataset = acme.Dataset(dataset)

    # Create the networks to optimize
    networks = acme.Networks(env, dataset, batch_size, random_seed)

    # Create the learner
    learner = acme.Learner(networks, learning_rate, discount, target_update_period, use_sarsa_target)

    # Define the evaluator network
    evaluator = acme.Evaluator(env, networks, batch_size, random_seed)

    # Create the actor and the environment loop
    actor = acme.Actor(env, networks, batch_size, random_seed)
    environment_loop = acme.EnvironmentLoop(env, actor, evaluator, batch_size, random_seed)

    # Run the environment loop
    environment_loop.run(num_demonstration_episodes, evaluation_period)

def add_next_action_extras(transition):
    # Add the next action extras to the transition
    next_action_extras = acme.NextActionExtras(transition.next_state, transition.next_action)
    transition.next_action_extras = next_action_extras

# Main function
if __name__ == '__main__':
    # Create the CRR agent
    create_crr_agent('CartPole-v1', 'cartpole_demonstrations.hdf5', 32, 100, 1000, 0, 0.001, 0.99, 10, True)

    # Add the next action extras to the transitions
    add_next_action_extras(transition)

    # Run the environment loop
    environment_loop.run(num_demonstration_episodes, evaluation_period)