Modeling Variation in Human Feedback with User Inputs: An Exploratory Methodology

摘要: To expedite the development process of interactive reinforcement learning (IntRL) algorithms, prior work often uses perfect oracles as simulated human teachers to furnish feedback signals. These oracles typically derive from ground-truth knowledge or optimal policies, providing dense and error-free feedback to a robot learner without delay. However, this machine-like feedback behavior fails to accurately represent the diverse patterns observed in human feedback, which may lead to unstable or unexpected algorithm performance in real-world human-robot interaction. To alleviate this limitation of oracles in oversimplifying user behavior, we propose a method for modeling variation in human feedback that can be applied to a standard oracle. We present a model with 5 dimensions of feedback variation identified in prior work. This model enables the modification of feedback outputs from perfect oracles to introduce …