Title: Entropy-Regularized Model-Based Offline Reinforcement Learning. Abstract: Model-based approaches to offline Reinforcement Learning (RL) aim to remedy the problem of sample complexity in offline learning via first estimating a pessimistic Markov Decision Process (MDP) from offline data, followed by freely exploring in the learned model for policy optimization. Recent advances in model-based RL techniques mainly rely on an ensemble of models to quantify the uncertainty of the empirical MDP which is leveraged to penalize out-of-distribution state-action pairs during the policy learning. However, the performance of ensembles for uncertainty quantification highly depends on  how they are implemented in practice, which can be a limiting factor. In this paper, we propose a systematic way to measure the epistemic uncertainty and present \abbrv, an Entropy-regularized Model-based Offline RL approach, to provide a smooth error estimation when leaving the support of data toward uncertain areas. Subsequently, we optimize a single neural architecture that maximizes the likelihood of offline data distribution while regularizing the transitions that are outside of the data support.  Empirical results demonstrate that our framework achieves competitive performance compared to state-of-the-art offline RL methods on D4RL benchmark datasets.