Hana Hoshino

OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching

2021 · arXiv (Cornell University)

Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious. However, prior IRL algorithms use on-policy transitions, which require intensive sampling from the current policy for stable and optimal performance. This …