  Considering generating samples with high rewards, we focus on optimizing deep
neural networks parameterized stochastic differential equations (SDEs), the
advanced generative models with high expressiveness, with policy gradient, the
leading algorithm in reinforcement learning. Nevertheless, when applying policy
gradients to SDEs, since the policy gradient is estimated on a finite set of
trajectories, it can be ill-defined, and the policy behavior in data-scarce
regions may be uncontrolled. This challenge compromises the stability of policy
gradients and negatively impacts sample complexity. To address these issues, we
propose constraining the SDE to be consistent with its associated perturbation
process. Since the perturbation process covers the entire space and is easy to
sample, we can mitigate the aforementioned problems. Our framework offers a
general approach allowing for a versatile selection of policy gradient methods
to effectively and efficiently train SDEs. We evaluate our algorithm on the
task of structure-based drug design and optimize the binding affinity of
generated ligand molecules. Our method achieves the best Vina score -9.07 on
the CrossDocked2020 dataset.
