title: Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
abstract: Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior.  The IRL setting is remarkably useful for automated control, in situations where the reward function is difficult to specify manually or as a means to extract agent preference. In this work, we provide a new IRL algorithm for the continuous state space setting with unknown transition dynamics by modeling the system using a basis of orthonormal functions. Moreover, we provide a proof of correctness and formal guarantees on the sample and time complexity of our algorithm.  Finally, we present synthetic experiments to corroborate our theoretical guarantees.
authors: Gregory Dexter Kevin Bello Jean Honorio
hIndex: Jean Honorio 17