Three Pathways to Neurosymbolic Reinforcement Learning with Interpretable Model and Policy Networks

Neurosymbolic AI combines the interpretability, parsimony, and explicit reasoning of classical symbolic approaches with the statistical learning of data-driven neural approaches. Models and policies that are simultaneously differentiable and interpretable may be key enablers of this marriage. This paper demonstrates three pathways to implementing such models and policies in a real-world reinforcement learning setting. Specifically, we study a broad class of neural networks that build interpretable semantics directly into their architecture. We reveal and highlight both the potential and the …

cs.ai cs.lg data data-driven differentiable interpretability key marriage networks paper policy reasoning reinforcement reinforcement learning statistical