Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies

TL;DR

This paper introduces constrained normalizing flow policies for reinforcement learning, providing interpretable, safe, and domain-knowledge-friendly models that satisfy safety constraints analytically and improve learning efficiency.

Contribution

It proposes a novel interpretable policy model using normalizing flows that guarantees safety constraints analytically, enhancing interpretability and safety in reinforcement learning.

Findings

Ensures safety constraints through analytically constructed normalizing flows.

Facilitates interpretability via a sequence of transformations on actions.

Maintains constraint satisfaction throughout the learning process.

Abstract

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 of these issues, we propose constrained normalizing flow policies as interpretable and safe-by-construction policy models. We achieve safety for reinforcement learning problems with instantaneous safety constraints, for which we can exploit domain knowledge by analytically constructing a normalizing flow that ensures constraint satisfaction. The normalizing flow corresponds to an interpretable sequence of transformations on action samples, each ensuring alignment with respect to a particular constraint. Our experiments reveal benefits beyond interpretability in an easier learning objective and maintained constraint satisfaction throughout the entire learning process. Our approach leverages constraints over reward engineering while offering enhanced interpretability, safety, and direct means of providing domain knowledge to the agent without relying on complex reward functions.