Constrained Normalizing Flow Policies for Interpretable and Safe Reinforcement Learning

Constrained normalizing flow policies enable interpretable and safe-by-construction reinforcement learning agents by exploiting domain knowledge to analytically construct invertible transformations that map actions into the allowed constraint regions.