We investigate using symbolic regression (SR) to model dynamics with mathematical expressions in model-based reinforcement learning (MBRL). While the primary promise of MBRL is to enable sample-efficient learning, most popular MBRL algorithms rely, in order to learn their approximate world model, on black-box over-parametrized neural networks, which are known to be data-hungry and are prone to overfitting in low-data regime. In this paper, we leverage the fact that a large collection of environments considered in RL is governed by physical laws that compose elementary operators e.g $\sin{},\sqrt{\phantom{x}}, \exp{}, \frac{\text{d}}{\text{dt}}$, and we propose to search a world model in the space of interpretable mathematical expressions with SR. We show empirically on simple domains that MBRL can benefit from the extrapolation capabilities and sample efficiency of SR compared to neural models.