Before revision:  Widely used reinforcement learning algorithms discretize continuous time and use the Bellman equation to estimate exponentially-discounted future reward . However, exponential discounting introduces a time scale to the computation of value. Scaling is a serious problem in continuous time: efficient learning with scaled algorithms requires prior knowledge of the relevant scale. That is, with scaled algorithms one must know at least part of the solution to a problem prior to attempting a solution .
After revision:  Widely used reinforcement learning algorithms discretize continuous time and use the Bellman equation to estimate exponentially-discounted future reward using the Bellman equation (model-free algorithms). An important drawback of model-based algorithms is that computational cost grows linearly with the solution to a problem prior to attempting a solution .