Cognitive Architecture for Introspecting Deep Reinforcement Learning Agents

Abstract

In this work we demonstrate the use of Cognitive Architectures in the Reinforcement Learning domain, specifically to serve as a common ground to understand and explain Reinforcement Learning agents in Human Ontology terms. Cognitive Architectures could potentially act as an adaptive bridge between Cognition and modern AI, sensitive to the cognitive dynamics of human user and the learning dynamics of AI agents.