Workers ending prematurely when using Tune

I'm trying to learn the basics of Tune. In the following script, I would expect each worker to run for 100 iterations and then end however, the workers end before reaching 100 iterations with state 3 ( TypeError? ). I do not see any error messages so I might be confused as to what is actually supposed to happen. Out of 10 samples, only 2 reach 100 iterations. The rest of the samples are between 5 and 16 iterations.

"""Testing Tune with CartPole."""
import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.bayesopt import BayesOptSearch

if __name__ == "__main__":
    tune_metric = "info/learner/default_policy/critic_loss"

    space = {"gamma": (0.01, 1)}
    algo = BayesOptSearch(
        space,
        metric=tune_metric,
        mode="min",
        utility_kwargs={
            "kind": "ucb",
            "kappa": 2.5,
            "xi": 0.0
        })
    scheduler = AsyncHyperBandScheduler(metric=tune_metric, mode="min")
    ray.init()
    analysis = tune.run(
        "SAC",
        stop={"training_iteration": 100},
        search_alg=algo,
        scheduler=scheduler,
        num_samples=10,
        config={
            "env": "CartPole-v0",
            },
    )

    print("Best config: ", analysis.get_best_config(metric=tune_metric,
                                                    mode="min"))

When I attempt to run the following example, the same thing occurs ( mnist pytorch trainable )