Safety-Aware Apprenticeship Learning
share
Apprenticeship learning (AL) is a class of "learning from demonstrations" techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations. In this paper, we study the problem of how to make AL algorithms inherently safe while still meeting its learning objective. We consider a setting where the unknown reward function is assumed to be a linear combination of a set of state features, and the safety property is specified in Probabilistic Computation Tree Logic (PCTL). By embedding probabilistic model checking inside AL, we propose a novel counterexample-guided approach that can ensure both safety and performance of the learned policy. We demonstrate the effectiveness of our approach on several challenging AL scenarios where safety is essential.
READ FULL TEXT