Learning Constraints on Autonomous Behavior from Proactive Feedback
Connor Basich,Saaduddin Mahmud,Shlomo Zilberstein,Connor Basich,Saaduddin Mahmud,Shlomo Zilberstein
Learning from feedback is a common paradigm to acquire information that is hard to specify a priori. In this work, we consider an agent with a known nominal reward model that captures its high-level task objective. Furthermore, the agent operates subject to constraints that are unknown a priori and must be inferred from human interventions. Unlike existing methods, our approach does not rely on fu...
