Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees

Part of Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

Authors

Gregory Dexter, Kevin Bello, Jean Honorio

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.