Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies

Finn Rietz, Erik Schaffernicht, Stefan Heinrich +1

Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains. To address both…