  We study a posterior sampling approach to efficient exploration in
constrained reinforcement learning. Alternatively to existing algorithms, we
propose two simple algorithms that are more efficient statistically, simpler to
implement and computationally cheaper. The first algorithm is based on a linear
formulation of CMDP, and the second algorithm leverages the saddle-point
formulation of CMDP. Our empirical results demonstrate that, despite its
simplicity, posterior sampling achieves state-of-the-art performance and, in
some cases, significantly outperforms optimistic algorithms.
