Grounding Insight: An Artificial Affect Signal for Human-Centric Explainability of RL Agents

arXiv:2611.09922v1 Announce Type: new Abstract: In Human-Interactive Robot Learning (HIRL), human teachers assess learning progress by observing agent behavior. However, Reinforcement Learning (RL) agents often fail to express learning progress in human-interpretable ways, leading to suboptimal or counterproductive teaching. This work proposes "Insight": a human-interpretable affective signal grounded in the RL process. It is specifically designed to address the "Absence of learning" human teacher mental model misalignment - the misconception that a failing or exploring agent is broken, underpowered or not learning at all - by signaling an agent's "Aha!" Insight moment. The signal is derived from the absolute Temporal Difference (TD) error making it easily implementable in other RL agents. To ground this signal, five design criteria were formulated: Relevance, Interpretability, Faithfulness, Scalability, Generalizability. The structural construct validity was assessed through simulation, examining micro and macro dynamics and event-related sensitivity. Micro dynamics reveal that Insight captures meaningful learning events at the appropriate time and magnitude. Macro dynamics demonstrate that the signal follows the agent's learning phases and reflects overall progress. Event-related sensitivity shows, the signal distinguishes the saliency of environment interactions, such as positive rewards versus "trauma-like" negative events. These results demonstrate that technical RL dynamics can be effectively packaged into human-like affective substrates to close the teacher-learner loop.