This paper deals with the problem of efficient sampling from a stochastic differential equation, given the drift function and the diffusion matrix.

Existing theoretical works put strict assumptions on the ANN (e.g.

We demonstrate that this assumption imposes performance limitations in particular on difficult problem instances.

Deep reinforcement learning (RL) has achieved remarkable successes in complex tasks, e.g Go [