Increasing Transparency of Reinforcement Learning using Shielding for Human Preferences and Explanations

The adoption of Reinforcement Learning (RL) in several human-centred applications provides robots with autonomous decision-making capabilities and adaptability based on the observations of the operating environment. In such scenarios, however, the learning process can make robots' behaviours unclear and unpredictable to humans, thus preventing a smooth and effective Human-Robot Interaction (HRI). As a consequence, it becomes crucial to avoid robots performing actions that are unclear to the user. In this work, we investigate whether including human preferences in RL (concerning the actions the robot performs during learning) improves the transparency of a robot's behaviours. For this purpose, a shielding mechanism is included in the RL algorithm to include human preferences and to monitor the learning agent's decisions. We carried out a within-subjects study involving 26 participants to evaluate the robot's transparency in terms of Legibility, Predictability, and Expectability in different settings. Results indicate that considering human preferences during learning improves Legibility with respect to providing only Explanations, and combining human preferences with explanations elucidating the rationale behind the robot's decisions further amplifies transparency. Results also confirm that an increase in transparency leads to an increase in the safety, comfort, and reliability of the robot. These findings show the importance of transparency during learning and suggest a paradigm for robotic applications with human in the loop.