This package provides implementations of common model architectures used in model-based RL,
including probabilistic and deterministic ensembles. All models in the library derive from
mbrl.models.Model. We provide a generic ensemble implementation,
mbrl.models.BasicEnsemble, that can be used to produce epistemic uncertainty estimates
for any subclass of Model. For efficiency considerations, some specific model implementations
also provide their own ensemble implementations, without having to rely on BasicEnsemble.
One such model is
mbrl.models.GaussianMLP, which can be used as a single model or as
an ensemble. Additionally, it can be used as a deterministic model
trained with MSE loss, or a parameterized Gaussian with mean and log variance outputs, trained
with negative log-likelihood.